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| Titel |
Evaluating the Met Office Unified Model simulated land surface temperature
(LST) in northwest India |
| VerfasserIn |
Jennifer Brooke, Chawn Harlow, Stuart Webster, Belen Gallego-Elvira |
| Konferenz |
EGU General Assembly 2017
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| Medientyp |
Artikel
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| Sprache |
en
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| Digitales Dokument |
PDF |
| Erschienen |
In: GRA - Volume 19 (2017) |
| Datensatznummer |
250148809
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| Publikation (Nr.) |
 EGU/EGU2017-13101.pdf |
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| Zusammenfassung |
| Surface temperature biases in northwest India in the Met Office Unified Model (UM) show
significant heterogeneity with distinct regions of warm and cold biases. This work will show
verification of model biases through ground-based, in-situ airborne and satellite observations
during the Interaction of Convective Organisation and Monsoon Precipitation, Atmosphere,
Surface and Sea (INCOMPASS) campaign in northern India between May - July 2016. The
INCOMPASS project is part of the “Drivers of Variability” which is a programme
funded jointly by Natural Environment Research Council (NERC), the Newton fund,
Indian Ministry of Earth Sciences (MoES) National Monsoon Mission, and the Met
Office.
MODIS climatological data and near-real time retrievals have been used to investigate the
spatial biases in LST and how they correlate with model surface cover. The surface
temperature biases (both warm and cold biases) in the INCOMPASS 4.4 km south Asia
limited area domain are more dominant in June than May; the May MODIS climatology
comparison showed the cold bias was most dominant between 72 and 75 oE, and in the June
climatology comparison the cold bias had extended to almost 80 oE. The spatial
distribution and magnitude of surface temperature biases and how they correlate with
surface vegetation cover in the northwest region was investigated for a number
of sub-regions. Region 1 (25 to 26 oN) was found to have the largest mean cold
surface temperature bias and a strong correlation coefficients between the surface
temperature biases and the IGBP vegetation fractional cover dataset with R2 of 0.81 (bare
soil) and 0.72 (grasses). This is further supported by the strong positive correlation
coefficients between the bare soil cover fraction and the cold surface temperature bias
between the INCOMPASS 4.4km model and MODIS climatology for both May and
June.
It will be shown that regions with warm surface temperature bias in northwest India are
not strongly correlated to surface cover fractions; two prominent regions of warm surface
temperature biases have been identified i) arid regions between 27 and 29 oN associated with
bare soil fractions greater than 85 % and ii) Ganges basin region between 29 and 30
oN.
Finally, the response of soil moisture to the land surface temperature biases
will be investigated. The UM operationally uses satellite derived soil moisture in
its NWP system; the ASCAT soil moisture product has been used to explore the
representation of soil moisture in order to investigate how well the UM captures the
seasonal dry down in soil moisture prior to the onset of the India monsoon season. |
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