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Development of Instability Index of GEO-KOMPSAT-2A Sung-Rae Chung 1, Myoung-Hwan Ahn 2, Su-Jeong Lee 2 1 KMA/NMSC, 2 Ewha Womans University 2014 Convection.

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Presentation on theme: "Development of Instability Index of GEO-KOMPSAT-2A Sung-Rae Chung 1, Myoung-Hwan Ahn 2, Su-Jeong Lee 2 1 KMA/NMSC, 2 Ewha Womans University 2014 Convection."— Presentation transcript:

1 Development of Instability Index of GEO-KOMPSAT-2A Sung-Rae Chung 1, Myoung-Hwan Ahn 2, Su-Jeong Lee 2 1 KMA/NMSC, 2 Ewha Womans University 2014 Convection Working Group Workshop, 7-11 April 2014, Zagreb, Croatia

2 2 Geo-KOMPSAT-2 Program GK-2A for the next generation Meteorological Imager and Space Weather monitoring GK-2B for the Ocean Color and Atmospheric Trace Gas monitoring

3 3 AMI (Geo-KOMPSAT-2A) [Source: Konig, 2002] Bands Band Name Center Wavelength Band Width (Max, um) Resolution (km) SNR NEdT(K) (240/300K) Radiometric Accuracy Min(um)Max(um) VNIR 1VIS % 2VIS % 3VIS % 4VIS % 5NIR % 6NIR % MWIR 7IR /0.21K 8IR /0.11K 9IR /0.11K 10IR /0.121K 11IR /0.11K LWIR 12IR /0.151K 13IR /0.21K 14IR /0.11K 15IR /0.21.1K 16IR /0.31.1K AMI (Advanced Meteorological Imager) 16 spectral bands

4 4 Satellite observed radiances Stability Indices (e.g. CAPE) Good at estimating non-linear relations Once the statistical relations are established, the retrieval can be made computationally very fast ( Konig, 2002 ), often with surprising accuracy (Gardner & Dorling, 1998) Training dataset should be complete (large enough to cover a variety of atmospheric phenomena, seasons, and locations) ( Blackwell & Chen, 2009 ) Artificial Neural Network (ANN) setup relation [Source: Konig, 2002] Statistical Retrieval of Instability Index

5 T, q profiles (radiosonde, satellite,..) T, q profiles (radiosonde, satellite,..) Work Process Flow 5 CAPE Final set of Objective selection profiles CAPE Simulated Radiance (I ) MODTRAN run SRF (SEVIRI for AMI) Theoretical T B Inverse of Planck eq. (establish relations) Best ANN Parameters determine Measured radiance (T B ) Real Application: CAPE direct estimation from GK2A AMI ANN training CAPE RangeTOTALselected 0 <= CAPE < ,1941, <= CAPE <20006,3601, <= CAPE1,375 TOTAL140,9294,125 IASI (SEVIRI coverage)

6 6 cir_day CAP E INPUT Total weights ( W IH xW HO ) circular day 0.09 circular time 0.22 Latitude 0.02 Longitude 0.32 Sat. zenith Tb Tb Tb Tb Tb Tb Tb strongest weight cir_time latitude longitude sat_zenith TB6.2 TB7.3 TB8.7 TB9.6 TB10.8 TB12.0 TB13.4 n 1 n 2 n 3 n 4 n 5 n 6 n 7 n 8 n 9 n 10 n 11 n 12 Input layerHidden layer Output layer 2 nd strongest weight * thickness of the arrows: relative magnitude of connection weights Combination of weights for the best ANN performance

7 Algorithm Validation 7 Radiosonde IASI (SEVIRI coverage) CAPE ANN training CAPE retrieved CAPE measured ANN with best parameter ANN with best parameter Radiances from SEVIRI 2.5min (super) rapid scan direct estimation Validation compare Future work

8 CAPE derived using ANN algorithm (20 June 2013) 8 09:02:1411:02:1313:02:12 15:02:1517:02:1420:02:13 Significant features - During the afternoon hours (at around 13:02 UTC) several convective clouds begin to pop up over the regions where the CAPE values are relatively high (marked with arrows and circles at 09:02 UTC image) and developed to the severe convective clouds (at 15:02 UTC) - High CAPE values around the leading edges of clouds induce a further development, while weaker CAPE values around the trailing edges result in weakened convective activities. Significance will be assessed with more case studies and quantitative validation. - No significant convection occurs over high CAPE areas in the morning images (at around 11:02 UTC marked with dashed blue arrows) which requires further investigation. 1,0002,0004,000 (J / kg)03,000

9 CAPE derived using ANN algorithm (20 June ~21 UTC) 9 1,0002,0004,000 (J / kg)03,000

10 Thank you!


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