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Titel |
Impact of local data assimilation on tropical cyclone predictions over the Bay of Bengal using the ARW model |
VerfasserIn |
M. M. Greeshma, C. V. Srinivas, V. Yesubabu, C. V. Naidu, R. Baskaran, B. Venkatraman |
Medientyp |
Artikel
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Sprache |
Englisch
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ISSN |
0992-7689
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Digitales Dokument |
URL |
Erschienen |
In: Annales Geophysicae ; 33, no. 7 ; Nr. 33, no. 7 (2015-07-03), S.805-828 |
Datensatznummer |
250121217
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Publikation (Nr.) |
copernicus.org/angeo-33-805-2015.pdf |
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Zusammenfassung |
The tropical cyclone (TC) track and intensity predictions over Bay of Bengal
(BOB) using the Advanced Research Weather Research and Forecasting (ARW) model are evaluated for a number of data
assimilation experiments using various types of data. Eight cyclones that
made landfall along the east coast of India during 2008–2013 were simulated.
Numerical experiments included a control run (CTL) using the National
Centers for Environmental Prediction (NCEP) 3-hourly 0.5 × 0.5°
resolution Global Forecasting System (GFS) analysis as the initial condition, and
a series of cycling mode variational assimilation experiments with Weather Research and Forecasting (WRF) data
assimilation (WRFDA) system using NCEP global PrepBUFR observations
(VARPREP), Atmospheric Motion Vectors (VARAMV), Advanced Microwave Sounding Unit (AMSU) A and B radiances (VARRAD)
and a combination of PrepBUFR and RAD (VARPREP+RAD). The impact of different
observations is investigated in detail in a case of the strongest TC,
Phailin,
for intensity, track and structure parameters, and finally also on a larger
set of cyclones. The results show that the assimilation of AMSU radiances and
Atmospheric Motion Vectors (AMV) improved the intensity and track predictions to a certain extent and the
use of operationally available NCEP PrepBUFR data which contains both
conventional and satellite observations produced larger impacts leading to
improvements in track and intensity forecasts. The forecast improvements are
found to be associated with changes in pressure, wind, temperature and
humidity distributions in the initial conditions after data assimilation.
The assimilation of mass (radiance) and wind (AMV) data showed different
impacts. While the motion vectors mainly influenced the track predictions,
the radiance data merely influenced forecast intensity. Of various
experiments, the VARPREP produced the largest impact with mean errors (India Meteorological
Department (IMD) observations less the model values)
of 78, 129, 166, 210 km in the vector track position, 10.3, 5.8, 4.8, 9.0 hPa
deeper than IMD data in central sea level pressure (CSLP) and 10.8, 3.9,
−0.2, 2.3 m s−1 stronger than IMD data in maximum surface winds (MSW) for 24,
48, 72, 96 h forecasts respectively. An improvement of about 3–36 % in
track, 6–63 % in CSLP, 26–103 % in MSW and 11–223 % in the radius
of maximum winds in 24–96 h lead time forecasts are found with VARPREP
over CTL, suggesting the advantages of assimilation of operationally
available PrepBUFR data for cyclone predictions. The better predictions with
PrepBUFR could be due to quality-controlled observations in addition to
containing different types of data (conventional, satellite) covering an
effectively larger area. The performance degradation of VARPREP+RAD with
the assimilation of all available observations over the domain after 72 h could
be due to poor area coverage and bias in the radiance data. |
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