New Observation Operators for Cloud Liquid/Ice Water Path From ABI and Their Impact on Assimilation and Hurricane Forecasts

Abstract

As powerful and deep tropical low-pressure systems, hurricane formation is always associated with cloud systems and precipitation, which is of large uncertainties caused by models' inability to correctly represent complex physical processes (Errico et al., 2007;Li et al., 2016). Therefore, hurricane forecast relies not only on a well-defined analysis of the environmental field in the clear sky area but also on providing an accurate initial field in the cloudy sky area. In addition to conventional observations, non-conventional observations

The Potential Value of Combining Assimilation of Water Vapor Radiances and Cloud Liquid/Ice Water Path From ABI on Analyses and Forecasts for Hurricane Irma (2017)

“…However, the ABIAS of MSLP and MWS are more complex for both experiments, and a smaller intensity bias of the EXP_ALL experiment can be seen after 36 hr, which the mean ABIAS of intensity also confirms. It should be noted that despite the additional assimilation of polar-orbiting satellite clear-sky radiance and ABI clear-sky radiance to the EXP_CLR experiment in this study, there seems to be a minor improvement in the forecast results compared to the control experiment in Meng et al (2021aMeng et al ( , 2021b, which only assimilates conventional observations. This could be mainly attributed to the difference in the choice of the cumulus parameterization scheme.…”

“…Therefore, observation errors must be carefully considered and tested when assimilating cloud property retrievals (Li et al, 2021). This study tested using constant observation errors like Meng et al (2021aMeng et al ( , 2021b, but it is hard to get enough data digested, especially from the thick clouds. Therefore, this study assigns the observation error as a function of LWP or IWP values based on the sensitivity test results.…”

“…In addition, Tropical Rainfall Measuring Mission (TRMM)/GPM hydrometeor retrievals have been assimilated into the Gridpoint Statistical Interpolation (GSI) and have shown considerable advantages in hurricane forecasting (Wu et al, 2016;Wu & Zupanski, 2017). Recently, the Cloud Water Path (CWP), including the LWP and IWP, derived from the Advanced Baseline Imager (ABI) (Schmit et al, 2005) onboard the new generation geostationary environmental satellites (GOES-R series) has been assimilated with the new observation operators developed by Meng et al (2021aMeng et al ( , 2021b, improving the hurricane track forecasts. However, their study focuses on validating the LWP & IWP observation operators, not on utilizing all-sky measurements of ABI.…”

“…Hence, it is crucial to accurately characterize the initial moisture conditions by the observations in the hurricane's inner core to produce intensity forecasts of TCs skillfully. The CWP from ABI could be a needed source of observation information for the hurricane inner-core areas and have been successfully assimilated into GSI with the newly developed observation operators built on the water vapor control variables (Meng et al, 2021a(Meng et al, , 2021b. Nevertheless, the mechanistic understanding regarding the CWP assimilation, especially combined with ABI clear radiances, on hurricane track and intensity forecasts has not been thoroughly investigated.…”

Satellite All-sky Infrared Radiance Assimilation: Recent Progress and Future Perspectives

“…However, hydrometeors are not used as control variables in some of the operational data assimilation systems, which makes it more difficult to assimilate information about the cloud field. As examples of the assimilation of IR-retrieved cloud products, Meng et al (2021a) revised an observation operator of cloud liquid/ice water path (LWP & IWP) to directly use the hydrometeor information from the NWP model, which reduced the differences between the observations and simulations. The assimilation of LWP and IWP products derived from the GOES-16 Advanced Baseline Imager (ABI) (Schmit et al, 2005) for Hurricane Irma (2017) shows improvement of water vapor both at analysis and in forecast fields.…”