Assimilation of GOES-13 Imager Clear-Sky Water Vapor (6.5 μm) Radiances into a Warn-on-Forecast System

Abstract

A prototype convection-allowing system using the Advanced Research version of the Weather Research and Forecasting (WRF-ARW) Model and employing an ensemble Kalman filter (EnKF) data assimilation technique has been developed and used during the spring 2016 and 2017 Hazardous Weather Testbeds. This system assimilates WSR-88D reflectivity and radial velocity, geostationary satellite cloud water path (CWP) retrievals, and available surface observations over a regional domain with a 3-km horizontal resolution at 15-min intervals, with 3-km initial conditions provided by an experimental High-Resolution Rapid Refresh ensemble (HRRR-e). However, no information on upper-level thermodynamic conditions in cloud-free regions is currently assimilated, as few timely observations exist. One potential solution is to also assimilate clear-sky satellite radiances, which provide information on mid- and upper-tropospheric temperature and moisture conditions. This research assimilates GOES-13 imager water vapor band (6.5 μm) radiances using the GSI-EnKF system to take advantage of the Community Radiative Transfer Model (CRTM) integration. Results using four cases from May 2016 showed that assimilating radiances generally had a neutral-to-positive impact on the model analysis, reducing humidity bias and/or errors at the appropriate model levels where verification observations were present. The effects on high-impact weather forecasts, as verified against forecast reflectivity and updraft helicity, were mixed. Three cases (9, 22, and 24 May) showed some improvement in skill, while the other (25 May) performed worse, despite the improved environment. This research represents the first step in designing a high-resolution ensemble data assimilation system to use GOES-16 Advanced Baseline Imager data, which provides additional water vapor bands and increased spatial and temporal resolution.

Assimilating Every‐10‐minute Himawari‐8 Infrared Radiances to Improve Convective Predictability

“…This high potential of assimilating the Himawari‐8 infrared radiances may contribute to mitigating the impacts of sudden local severe rainfall, especially in the region where a ground‐based radar is not installed. In addition, we would expect that convective predictability can be further improved if ground‐based radar and geostationary satellite observations are assimilated simultaneously (Cintineo et al, ; Jones et al, , ). The synergy of ground‐based radar and geostationary satellite observations on convective predictability should be explored in the future.…”

Assimilating All-Sky Infrared Radiances from GOES-16 ABI Using an Ensemble Kalman Filter for Convection-Allowing Severe Thunderstorms Prediction

“…A broad radius of influence (ROI) for vertical localization of 5 times the altitude AGL of each observation is used during the EnKF; considering that BT is an accumulated nonlocal type of observation, the length scale of vertical ROI is selected so that influences from observations will reduce by about 25% at both the model bottom and top under clearsky conditions. The horizontal ROI is fixed to 30 km for all BT observations, which is slightly narrower than the 40-60-km horizontal ROI that has been used in previous simulated and real ABI radiance assimilation studies with horizontal model grid spacing of 3-6 km (Jones et al 2015;Cintineo et al 2016;Honda et al 2018b); this study uses a much higher 1-km resolution for the numerical model together with raw ABI radiance observations.…”

“…The EnKF has the advantage of providing flow-dependent time-varying estimation of background error covariances, compared with variational-based assimilation techniques like 3DVar (Zhang et al 2011), and thus is widely used in data assimilation applications for severe thunderstorms at convection-allowing storm scales (e.g., Snyder and Zhang 2003;Aksoy et al 2009;Dowell et al 2011;Wheatley et al 2015;Yussouf et al 2015;Yokota et al 2016). Observing system simulation experiments (OSSEs) of the direct assimilation of synthetic ABI BT observations using EnKF have mostly focused on extratropical cyclones (Otkin 2010(Otkin , 2012Zupanski et al 2011;Jones et al 2013), mesoscale convective systems (Jones et al 2014;Cintineo et al 2016), or tropical cyclones (F. Zhang 2017, 2018a); recently, Honda et al (2018a,b) assimilated real-data all-sky radiance observations from the Advanced Himawari Imager (AHI) on board the Himawari-8 satellite, which has similar infrared channels as the ABI onboard the GOES-16 satellites, to improve predictions of tropical cyclones and associated torrential precipitation and floods. However, stormscale data assimilation studies using geostationary satellite observations only assimilated temperature and moisture profile retrievals (Jones et al 2017), cloud-top temperature (Kerr et al 2015), water paths of different hydrometeor species (Jones and Stensrud 2015;Jones et al 2015Jones et al , 2016, and GOES-13 clear-sky infrared radiance (Jones et al 2018), rather than all-sky (clear sky and cloud affected) infrared radiance observations from high spatiotemporal imagers like the ABI.…”

“…Observing system simulation experiments (OSSEs) of the direct assimilation of synthetic ABI BT observations using EnKF have mostly focused on extratropical cyclones (Otkin 2010(Otkin , 2012Zupanski et al 2011;Jones et al 2013), mesoscale convective systems (Jones et al 2014;Cintineo et al 2016), or tropical cyclones (F. Zhang 2017, 2018a); recently, Honda et al (2018a,b) assimilated real-data all-sky radiance observations from the Advanced Himawari Imager (AHI) on board the Himawari-8 satellite, which has similar infrared channels as the ABI onboard the GOES-16 satellites, to improve predictions of tropical cyclones and associated torrential precipitation and floods. However, stormscale data assimilation studies using geostationary satellite observations only assimilated temperature and moisture profile retrievals (Jones et al 2017), cloud-top temperature (Kerr et al 2015), water paths of different hydrometeor species (Jones and Stensrud 2015;Jones et al 2015Jones et al , 2016, and GOES-13 clear-sky infrared radiance (Jones et al 2018), rather than all-sky (clear sky and cloud affected) infrared radiance observations from high spatiotemporal imagers like the ABI. This study is the first attempt to directly assimilate realworld all-sky ABI infrared BT observations using an EnKF approach with a numerical model running at a convection-allowing 1-km horizontal resolution (finer than all previous studies) to improve model forecasts of a tornadic thunderstorm event.…”

Assimilation of All-Sky SEVIRI Infrared Brightness Temperatures in a Regional-Scale Ensemble Data Assimilation System

“…Moreover, unlike polar-orbiting satellites, geostationary sensors are also able to provide frequent observation updates that cover most, if not all, of the model domain. Some recent studies have shown positive results when assimilating satellite-derived prod-ucts such as cloud water path or layer precipitable water (Jones et al 2013b(Jones et al , 2016(Jones et al , 2018Schomburg et al 2015;Kerr et al 2015;Wang et al 2018), whereas other studies have explored the direct assimilation of all-sky infrared brightness temperatures.…”