Assimilation of GOES-16 satellite derived winds into the warn-on-forecast system

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

“…The wind observations are passed through a QC module to filter out bad data, before their insertion into the analysis procedure. Based on the channel-dependent pressure ranges for AMVs (Daniels et al, 2012) and the QC strategy adopted by previous studies (Kim et al, 2017;Lim et al, 2019;Mallick & Jones, 2020;Sawada et al, 2019;Velden et al, 2017), the seven-step observation checks depending on retrieval band are applied as the following:…”

“…A relaxed gross error check, which is designed to increase the retention of winds representing smaller-scale flow, is performed to eliminate the observations outside of set tolerances from the interpolated model background field. Similar to Mallick and Jones (2020), the threshold value between the ratio of the innovation to the observation error is set to 5.…”

“…New‐generation geostationary satellites, such as Geostationary Operational Environmental Satellites‐16/17 (GOES‐16/17), Himawari‐8/9, Geostationary Korea Multipurpose Satellite‐2A (GEO‐KOMPSAT‐2A/2B), EUMETSAT‐Meteosat Third Generation, and Fengyun‐4, and so on, can provide high‐spatiotemporal‐resolution atmospheric motion vector (AMV) products containing mesoscale or convective flow information. Recent studies showed that assimilating high‐resolution AMVs has generally slightly positive or neutral impacts on the global and regional model analyses and forecasts, especially with small reductions in wind errors (Cherubini et al., 2006; Elsberry et al., 2018; James & Benjamin, 2017; Kim & Kim, 2018; Kim et al., 2017; Le Marshall et al., 2008; Lean et al., 2016; Lean & Bormann, 2019; Li et al., 2020; Lim et al., 2019; Mallick & Jones, 2020; Otsuka et al., 2015; Sawada et al., 2019; Velden et al., 2017; Wu et al., 2015; Yamashita, 2012, 2017). Most of these studies focused on improving the hurricane, or tropical cyclone track and intensity forecasts by assimilating high‐density or rapid‐scan AMVs into a variational data assimilation (DA) or ensemble‐based Kalman filter framework.…”

“…Most of these studies focused on improving the hurricane, or tropical cyclone track and intensity forecasts by assimilating high‐density or rapid‐scan AMVs into a variational data assimilation (DA) or ensemble‐based Kalman filter framework. However, the impact of high‐spatiotemporal‐resolution AMVs on mesoscale and convective scale weather forecasts over land has not been extensively explored (e.g., Mallick & Jones, 2020). The main purpose of this two‐part study is to investigate the impact of high‐resolution AMVs on convective scale DA and numerical weather prediction (NWP).…”

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

“…Bedka and Mecikalski (2005) demonstrated that the high‐density AMVs derived from GOES‐12 using a modified University of Wisconsin—Madison Cooperative Institute for Meteorological Satellite Studies retrieval algorithm can provide mesoscale flow information including low‐level (1,000–700 hPa) convergence and midlevel (700–400 hPa) to upper‐level (400–100 hPa) divergence, which is important for monitoring the evolution of deep convection. Recent studies showed that assimilating high‐resolution AMVs has generally slightly positive or neutral impacts on the global and regional model analyses and forecasts, especially with small reductions in wind errors (Cherubini et al., 2006; Elsberry et al., 2018; Kim & Kim, 2018; M. Kim et al., 2017; Le Marshall et al., 2008; Lean et al., 2016; Lean & Bormann, 2019; Li et al., 2020; Lim et al., 2019; Mallick & Jones, 2020; Otsuka et al., 2015; Sawada et al., 2019; Velden et al., 2017; Wu et al., 2015; Yamashita, 2012, 2017). However, most of these studies focus on improving the tropical cyclone track and intensity forecasts by assimilating high‐density or rapid‐scan AMVs into a variational data assimilation or ensemble based Kalman filter framework, the impact of high‐spatiotemporal‐resolution AMVs on mesoscale and convective‐scale weather forecasts over land have not been extensively explored (e.g., Mallick & Jones, 2020).…”