Abstract

This study assesses the impact assimilating the scatterometer near-surface wind observations and total precipitable water from the SSMI, into WRF on genesis and track forecasting of four tropical cyclones (TCs). These TCs are selected to be representative of different intensity categories and basins. Impact is via a series of data denial experiments that systematically exclude the remote sensed information. Compared with the control case, in which only the final analysis atmospheric variables are used to initialize and provide the lateral boundary conditions, the data assimilation runs performed consistently better, but with very different skill levels for the different TCs. Eliassen-Palm flux analyses are employed. It is confirmed that if a polar orbital satellite footprint passes over the TC’s critical genesis region, the forecast will profit most from assimilating the remotely sensed information. If the critical genesis region lies within an interorbital gap then, regardless of how strong the TC later becomes (e.g., Katrina 2005), the improvement from assimilating near-surface winds and total precipitable water in the model prediction is severely limited. This underpins the need for a synergy of data from different scatterometers/radiometers. Other approaches are suggested to improve the accuracy in the prediction of TC genesis and tracks.

Highlights

  • Weather and climate hazards are causing increasingly more damage [1]

  • This study focuses on improving Tropical cyclones (TCs) track forecasting by further assimilating remotely sensed, near-surface (e.g., 10 m above sea level) oceanic wind fields and total precipitable water (TPW)

  • Four prediction experiments are designed to test the performance of data assimilation: (1) without assimilating QSCAT near-surface winds or SSM/I TPW, (2) assimilating QSCAT at 06 UTC on 16 October (i.e., Q’s in Table 2), (3) further assimilating QSCAT at 06 UTC on 17 October based on experiment 2 (QS’s), and (4) further assimilating SSMI at 12 UTC on 17 October based on experiment 3 (QSQ’s)

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Summary

Introduction

Weather and climate hazards are causing increasingly more damage [1]. In recent years, there has been a sharp rise in human and property loss from weather- and climate-related disasters. This study focuses on improving TC track forecasting by further assimilating remotely sensed, near-surface (e.g., 10 m above sea level) oceanic wind fields and total precipitable water (TPW). IR and microwave sounders provided water vapour profiles (e.g., from many polar orbiters such as the Aqua A-Train and NOAA POES) These information sources have proved to be extremely valuable in improving numerical weather prediction (NWP) model skill [8,9,10]. An assessment is made of the added value of assimilating surface winds from QuikSCAT scatterometer (hereafter, QSCAT) and the Advanced Scatterometer (ASCAT, http://www.remss.com/missions/ascat [20]) and total precipitable water from the Special Sensor Microwave Imager (SSMI [21]). The assessment was based on a comparison of the forecasts with the ERA-Interim reanalyses

The Data Assimilation Procedure and Quality Control
Numerical Experiments
Results
Summary
2: SQ 3: S2Q
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