Satellite radiance assimilation using a 3DVAR assimilation system for hurricane Sandy forecasts

Tanvir Islam, Prashant K. Srivastava, Dinesh Kumar, George Petropoulos, Qiang Dai, Lu Zhuo

Research output: Contribution to journalArticlepeer-review

2 Citations (SciVal)

Abstract

In this article, we present an assimilation impact study for forecasting hurricane Sandy using a three‐dimensional variational data assimilation system (3DVAR). In particular, we employ the 3DVAR component of the Weather Research and Forecasting Model and conduct analysis/forecast cycling experiments for “control” and “radiance” assimilation cases for the hurricane Sandy period. In “control” assimilation experiment, only conventional air and surface observations data are assimilated, while, in “radiance” assimilation experiment, along with the conventional air and surface observations data, the satellite radiance data from the Advanced Microwave Sounding Unit-A (AMSU-A) and the Microwave Humidity Sounder (MHS) sensors are also assimilated. For the radiance assimilation, we employ the community radiative transfer model as the forward operator and perform quality control and bias correction procedure before the radiance data are assimilated. In order to assess the impact of the assimilation experiments, we produce 132-h deterministic forecast starting on 00 UTC October 25, 2012. The results reveal that, in particular, the assimilation of AMSU-A satellite radiances helps to improve the short- to medium-range forecast (up to ~60-h lead time). The forecast skill is degraded in the long-range forecast (beyond 60 h) with the AMSU-A assimilation.
Original languageEnglish
Pages (from-to)845-855
Number of pages11
JournalNatural Hazards
Volume82
Issue number2
DOIs
Publication statusPublished - 19 Feb 2016

Keywords

  • variational data assimilation
  • Numerical weather predicition (NWP)
  • cyclone forecast
  • track propagation
  • WRF 3DVAR
  • radiative transfer
  • ATOVS
  • AMSU-A
  • AMSU-B
  • MHS

Fingerprint

Dive into the research topics of 'Satellite radiance assimilation using a 3DVAR assimilation system for hurricane Sandy forecasts'. Together they form a unique fingerprint.

Cite this