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). Issue 19 (6th October 2022)

• Record Type: Journal Article
• Authors: Meng, Deming; Chen, Yaodeng; Li, Jun; Tan, Zhe‐Min; Wang, Pei; Lee, Jung‐Rim; Li, Jinlong; Li, Zhenglong
• Is Part Of: Journal of geophysical research. Volume 127:Issue 19(2022)
• Page Start: n/a
• Page End: n/a
• Publication Date: 2022-10-06
• Subjects: geostationary satellite assimilation -- all‐sky assimilation -- cloud observations -- observation operator -- hydrometeor -- hurricane; Atmospheric physics -- Periodicals; Geophysics -- Periodicals; 551.5
• DOI: 10.1029/2022JD036910
• Languages: English
• ISSNs: 2169-897X

Abstract

Abstract: All‐sky assimilation of infrared (IR), near‐IR, and visible band measurements are beneficial for forecasting cloud‐covered tropical cyclones, but numerous challenges remain. This study uses the combined assimilation of clear‐sky water vapor radiances and the cloud liquid/ice water path (CWP) retrieved from the Advanced Baseline Imager on the new generation geostationary environmental satellites. The two measurements are spatial complementary to one another. Microphysics schemes, cumulus parameterizations, and dynamic CWP observation errors are optimized using sensitivity tests. The cycling assimilations and subsequent forecasts of Hurricane Irma (2017) are investigated to assess the added value of the combined assimilation and its impacts on hurricane forecasts. Comparisons with ERA5 and dropsonde observations from the National Hurricane Operations Plan show that the combined assimilation can improve the prognostic variables of Hurricane Irma (2017) for both the large‐scale circumstances and the inner‐core area, resulting in a clear improvement in hurricane track and intensity predictions. The detailed diagnostics show that the improved track predictions can be attributed to the more precise steering flow produced by the combined assimilation. Moreover, the additional CWP assimilation allows for a more accurate initialization of water vapor in the hurricane's inner‐core area when the model produces dry simulated water vapor bias. Then the subsequent phase changes of water vapor improve the hurricane intensity forecasting by enhancing the feedback between heating and vortex circulation. The combined assimilation shows the potential value of better utilization of all‐sky satellite measurements to improve hurricane initialization and forecast. Plain Language Summary: Tropical cyclones (TCs) are huge cloud‐based weather systems. Using all‐sky satellite data can improve TC initializations, thus improving TC forecasts. Usually, direct assimilation of radiance and indirect assimilation of retrievals are adopted to initialize TC. The direct method is relatively mature in the clear‐sky, while the indirect method is more straightforward for cloudy regions. Therefore, this study makes combined use of clear‐sky water vapor radiances with the cloud liquid/ice water path (CWP) data from the Advanced Baseline Imager (ABI) on the new generation geostationary environmental satellites (GOES‐R). The two observations can complement each other to maximize the use of ABI observations. The evaluation shows that the combined use of the two observations improves the large‐scale environmental fields of Hurricane Irma (2017), which simulates more accurately directing airflow and, as a result, better track forecasts. The improved prediction variables in the inner‐core area result from additional CWP use, which improves the water vapor initialization. The subsequent phase change of water vapor propagates the cloud information to the other forecast variables, thus improving the intensity forecasts. Overall, the study demonstrates the potential value of better combining utilizing the satellite's clear‐sky and cloudy observations and improving hurricane analysis and forecasting. Key Points: The spatial complementarity of clear and cloudy satellite data facilitates the combined assimilation implementation for hurricane forecasts The combined assimilation makes full use of satellite data to reduce errors at a large scale and then contributes to better steering flow Cloud observation assimilation may improve hurricane intensity forecast by enhancing the feedback between heating and vortex circulation