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Citations
... However, the benefit of AMVs is dependent on their quality control, error characteristics and data selection [13]. Due to the limitation caused by AMVs giving information mainly at a single level of the troposphere in the data assimilation of NWP [32,33], the height assignment of the tracers is considered the main source of AMV observation errors [13]. Over the last few decades, the quality of operational AMVs has undergone continuous improvements. ...
The ever-increasing capacity of numerical weather prediction (NWP) models requires accurate flow information at higher spatial and temporal resolutions. The atmospheric motion vectors (AMVs) extracted from the Advanced Geostationary Radiation Imager (AGRI) mounted on the Fengyun-4A (FY-4A) satellite can provide information about atmospheric flow fields on small scales. This study focused on the assimilation of FY-4A AMVs and its impact on forecasts in the regional NWP system of the China Meteorological Administration—Beijing (CAM-BJ). The statistical characterization of FY-4A AMVs was firstly analyzed, and an optimal observation error in each vertical level was obtained. Three groups of retrospective runs over a one-month period were conducted, and the impact of assimilating the AMVs with different strategies on the forecasts of the CMA-BJ system were compared and evaluated. The results suggested that the optimal observation errors reduced the standard deviation of the background departures for U and V wind, leading to an improvement in the standard deviation in the corresponding analysis departures of about 8.3% for U wind and 7.3% for V wind. Assimilating FY-4A AMV data with a quality indicator (QI) above 80 and the optimal observation errors reduced the error of upper wind forecast in the CMA-BJ system. A benefit was also obtained in the error of surface wind forecast after 6 h of the forecasts, although it was not significant. For rainfall forecast with different thresholds, the score skills increased slightly after 6 h of the forecasts. There was an overall improvement for the overprediction of 24 h accumulated precipitation forecast including the AMVs, even when conventional observations were relatively rich. The application of FY-4A AMVs with a QI > 80 and adjustment to observation errors has a positive impact on the upper wind forecast in the CMA-BJ system, improving the score skill of rainfall forecasting.
... On the other hand, it is still difficult to assimilate AMV data in NWP models due to their complicated errors [22][23][24]. AMV data are typically treated as single-level observations retrieved from satellite radiances which measure the signals from a finite layer of the atmosphere rather than a specific level [25]. Various errors can be introduced in the reversion process, such as cloud-tracking errors, height errors, and spatially and temporally correlated errors [24,26]. ...
Atmospheric motion vectors (AMVs) derived from images of the geostationary satellite, Fengyun-4A (FY-4A), can provide high-spatiotemporal-resolution wind observations in the atmospheric middle and upper levels. To explore the potential benefits of these data for the numerical forecasting of severe weather events, the characteristics of FY-4A AMVs in different channels were analyzed and three groups of assimilation experiments were conducted in this study. The impacts of FY-4A AMVs on the forecasts of the rainstorm that occurred in Henan province in China on 20 July 2021, were investigated based on the Weather Research and Forecasting (WRF) model. The results show that FY-4A AMVs with a higher quality indicator (QI) exhibited a lower error characteristic at the cost of a reduced sample size. The assimilation of FY-4A AMVs reduced the error of the upper-level wind fields in 24 h forecasts. A positive impact could also be obtained for 10 m wind in 24 h forecasts, with an improvement of up to 9.74% for the mean bias and 3.0% for the root-mean-square error due to the inclusion of FY-4A AMVs with a QI > 70. Assimilating the AMVs with a QI > 80, there was an overall positive impact on the CSI score skills of 6 h accumulated precipitation above 1.0 mm in the 24 h forecast. A significant improvement could be found in the forecasting of heavy rainfall above 25.0 mm after 6 h of the forecast. The spatial distribution of the 24 h accumulated heavy rainfall zone was closer to the observations with the assimilation of the FY-4A AMVs. The adjustment of the initial wind fields resulting from the FY-4A AMVs brought a clear benefit to the quantitative precipitation forecasting skills in the event of the Henan 7.20 rainstorm; however, the AMV data assimilation still had difficulty in capturing the hourly maximum rainfall and intensity well.
... In this type of framework, a simulation from a high-resolution model provides the " true " atmosphere, from which sequences of satellite images are generated which are subsequently used to derive AMVs. In the first part (Hernandez-Carrascal et al., 2012 ), we introduced the simulation used in this study, analysed the realism of the resulting simulated images, and investigated the characteristics of the derived AMVs by comparing these to the " true " wind from the model simulation. For the latter, we employed the traditional approach of interpreting AMVs as single-level point observations. ...
... In this section, we give a short description of the simulation and datasets used in the study. The reader is referred to the companion paper (Hernandez-Carrascal et al., 2012) for details. The model used for the simulation is the Weather Research and Forecasting (WRF) regional model, with a nominal horizontal resolution of 1.7-3 km. ...
... These could particularly affect the realism of the optical thickness of some cirrus clouds in the WRF simulation. While our analysis of brightness temperature characteristics did not show large discrepancies between simulations and observations (Hernandez-Carrascal et al., 2012), more subtle differences can not be ruled out. It may also be partly due to a bias in the CLA (cloud analysis) product used to estimate the cloud top pressure in the AMV derivation prototype. ...
The work described here is part of a wider study whose main objective is to improve the characterization of Atmospheric Motion Vectors (AMVs) and their errors to improve the use of AMVs in Numerical Weather Prediction (NWP). AMVs are estimates of atmospheric wind derived by tracking apparent motion across sequences of meteorological satellite images, and it is known that they tend to exhibit considerable systematic and random errors and geographically varying quality, as shown in comparisons against radiosonde or NWP data. However, there is a rather limited knowledge of the characteristics and origin of these errors, although the height assignment is generally recognized as a key source of error. An important difficulty in the study of AMV errors is the scarcity of collocated observations of cloud and wind. To overcome that difficulty, we approach the analysis of AMV errors using a simulation framework: geostationary imagery is generated from a high resolution NWP model simulation, and AMVs are derived from sequences of simulated images. The NWP model provides a " ground truth " , which allows a detailed study of AMV errors, bypassing the usual difficulty of the scarcity of observations. Provided model simulations are realistic, the analysis of AMV errors in this setting can shed light on the nature of AMVs derived from observed imagery and their errors. The study is performed on the basis of Meteosat-8 simulations from the Weather Research and Forecasting (WRF) regional model, which has a nominal horizontal resolution of 3 km. This paper focusses on the part of the study that explores the assignment of AMVs to levels related to model clouds. Section 1 introduces the paper, and section 2 gives a brief description of the NWP model simulation and the AMV derivation. Section 3 presents comparisons between the simulated AMVs and the model " true " winds, exploring several possible ways of attributing height to the AMVs. Section 4 focusses on multi-layer situations, highlighting some of the problems associated, and illustrating how a simulation framework may help to study these situations. Finally, section 5 presents the conclusions.
The main objective of this study is to improve the characterization of Atmospheric Motion Vectors (AMVs) and their errors to improve the use of AMVs in Numerical Weather Prediction (NWP). It is known that AMVs tend to exhibit considerable systematic and random errors and geographically varying quality, as shown in comparisons against radiosonde or NWP data. However, there is a rather limited knowledge of the characteristics and origin of these errors: they can arise in the AMV derivation process, but they can also arise from the interpretation of AMVs as single-level point observations of wind. An important difficulty in the study of AMV errors is the scarcity of collocated observations of clouds and wind. To overcome that difficulty, this study approaches the analysis of AMV errors using a simulation framework in which AMVs are derived from sequences of images simulated from atmospheric forecast model data. In this framework the model provides a "ground truth", including wind and cloud distributions, which allows a detailed study of AMV errors. Provided model simulations are realistic, the analysis of AMV errors in this setting can shed light on the nature of AMVs derived from observed imagery and their errors. The model used for the simulation is the Weather Research and Forecasting (WRF) regional model, and the nominal horizontal resolution of the simulation is 3km. This presentation shows the main results of the ongoing study. First, cloud structures from observed and simulated images are compared. Then AMVs, interpreted as single-level point estimates of wind, are evaluated by comparison to the model truth. Then we present results regarding horizontal, vertical and temporal error correlations. Finally, we evaluate AMVs interpreted as vertical and horizontal averages of wind.
