Generation of Skillful Forecasts at Station scale through Objective Bias Correction: Multiple Site Evaluation for Summer Surface Temperature
IR@C-MMACS: CSIR-Centre for Mathematical Modelling and Computer Simulation, Bangalore
View Archive Info| Field | Value | |
| Title |
Generation of Skillful Forecasts at Station scale through Objective Bias Correction:
Multiple Site Evaluation for Summer Surface Temperature
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| Creator |
Goswami, P
Mallick, S |
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| Subject |
Climate Modelling
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| Description |
One factor that limits skill of the numerical models is the bias in the model forecasts with respect to
observations. Similarly, while the meso-scale models today can support horizontal grid spacing
down to a few kilometers or less, downscaling of model forecasts to arrive at station scale values
will remain a necessary step for many applications. While generic improvement in model skill
requires parallel and comprehensive development in model and other forecast methodology, one
way of achieving skill in station scale forecasts without (effort-intensive) calibration of model is to
implement an objective bias correction (referred to as debiasing). We show that a non-linear
objective debiasing can transform zero-skill forecasts from a meso-scale model (MM5) to forecasts
with significant skill. We consider 12 locations over India representing urban sites in different
geographical conditions during May-August, 2009. The model MM5 was integrated for 24 hours with
initial conditions from [global gridded analysis (FNL)] of the National Centers for Environmental
Prediction Global Forecast System (Final) for each of the days of May-August, 2009 in a completely
operational setting (without assuming any observed information on dynamics beyond the time of the
initial condition). It is shown that for all the locations and the four months, the skill of the debiased
forecast is significant against essentially zero skill of raw forecasts. The procedure provides an
applicable forecast strategy to attain realizable significant skill in station scale forecast. Potential
skill, derived using in-sample data for calibrating the debiasing parameters, shows promise of
further improvement with large samples.
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| Publisher |
CSIR Centre for Mathematical Modelling and Computer Simulation
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| Date |
2011-01
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| Type |
Monograph
NonPeerReviewed |
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| Format |
application/pdf
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| Identifier |
http://cir.cmmacs.ernet.in/265/1/rrcm1101.pdf
Goswami, P and Mallick, S (2011) Generation of Skillful Forecasts at Station scale through Objective Bias Correction: Multiple Site Evaluation for Summer Surface Temperature. Technical Report. CSIR Centre for Mathematical Modelling and Computer Simulation , C-MMACS,Bangalore 560037,India. (Unpublished) |
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| Relation |
http://cir.cmmacs.ernet.in/265/
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