정지기상위성자료를 이용한 중규모 바람장 산출 알고리즘 최적화
Optimization of Mesoscale Atmospheric Motion Vector Algorithm Using Geostationary Meteorological Satellite Data
1
조회
0
다운로드
4
인용
0
참고문헌
키워드
초록
The Atmospheric motion vectors (AMVs) derived using infrared (IR) channel imagery of geostationary satellites have been utilized widely for real-time weather analysis and data assimilation into global numerical prediction model. As the horizontal resolution of sensors on-board satellites gets higher, it becomes possible to identify atmospheric motions induced by convective clouds (meso-β and meso-γ scales). The National Institute of Meteorological Research (NIMR) developed the high resolution visible (HRV) AMV algorithm to detect mesoscale atmospheric motions including ageostrophic flows. To retrieve atmospheric motions smaller than meso-β scale effectively, the target size is reduced and the visible channel imagery of geostationary satellite with 1 km resolution is used. For the accurate AMVs, optimal conditions are decided by investigating sensitivity of algorithm to target selection and correction method of height assignment. The results show that the optimal conditions are target size of 32 km × 32 km, the grid interval as same as target size, and the optimal target selection method. The HRV AMVs derived with these conditions depict more effectively tropical cyclone OMAIS than IR AMVs and the mean speed of HRV AMVs in OMAIS is slightly faster than that of IR AMVs. Optimized mesoscale AMVs are derived for 6 months (Feb. 2010-Jun. 2010) and validated with radiosonde observations, which indicates NIMR's HRV AMV algorithm can retrieve successfully mesoscale atmospheric motions.
Abstract
The Atmospheric motion vectors (AMVs) derived using infrared (IR) channel imagery of geostationary satellites have been utilized widely for real-time weather analysis and data assimilation into global numerical prediction model. As the horizontal resolution of sensors on-board satellites gets higher, it becomes possible to identify atmospheric motions induced by convective clouds (meso-β and meso-γ scales). The National Institute of Meteorological Research (NIMR) developed the high resolution visible (HRV) AMV algorithm to detect mesoscale atmospheric motions including ageostrophic flows. To retrieve atmospheric motions smaller than meso-β scale effectively, the target size is reduced and the visible channel imagery of geostationary satellite with 1 km resolution is used. For the accurate AMVs, optimal conditions are decided by investigating sensitivity of algorithm to target selection and correction method of height assignment. The results show that the optimal conditions are target size of 32 km × 32 km, the grid interval as same as target size, and the optimal target selection method. The HRV AMVs derived with these conditions depict more effectively tropical cyclone OMAIS than IR AMVs and the mean speed of HRV AMVs in OMAIS is slightly faster than that of IR AMVs. Optimized mesoscale AMVs are derived for 6 months (Feb. 2010-Jun. 2010) and validated with radiosonde observations, which indicates NIMR's HRV AMV algorithm can retrieve successfully mesoscale atmospheric motions.
참고문헌 (22)
- Bedka, K. M., "Application of satellite-derived atmospheric motion vectors for estimating mesoscale flows", Appl. Meteor., 44, pp.1761~1772 (2005)
- Borde, R, "Improvements to EUMETSAT AMVs", Proc. 10th Int. Winds Workshop, Tokyo, Japan, pp.56~ (2010)
- Carranza M, "Atmospheric motion vectors derived from MSG rapid scanning service data at EUMETSAT", Proc. 10th Int. Winds Workshop, Tokyo, Japan, pp.56~ (2010)
- EUMETSAT, "MSG Meteorological products extraction facility algorithm specification documents., v5", ~ (2009)
- Forsythe, M, "AMV monitoring: results from the 3rd NWP SAF analysis", Proc. 9th Int. Winds Workshop, Annapolis, Maryland, USA (2008)
- Fritz, S, "Synoptic use of radiation measurements from satellite TIROS-II", Mon. Wea. Rev, 90, pp.1~9 (1962)
- Garca Pereda, J, "New developments in the high resolution winds product (HRW), at the satellite application facility on support to nowcasting & very short range forecasting (NWCSAF)", Proc. 10th Int. Winds Workshop, Tokyo, Japan, pp.56~ (2010)
- Genkova, I., "Global Atmospheric Motion Vector intercomparison study", Proc. 10th Int. Winds Workshop, Tokyo, Japan, pp.56~ (2010)
- Hasler, A. F., "In Situ Aircraft Verification of the Quality of Satellite Cloud Winds over Oceanic Regions", J. appl. Met, 18, pp.1481~1489 (1979)
- Hillger, D. W, "Estimating noise levels of remotely sensed measurements from satellites using spatial structure analysis", J. Atmos. Oceanic Technol, 5, pp.206214~ (1988)
- Holmlund, K., "The utilization of statistical propertied of satellite-derived atmospheric motion vectors to derive quality indicators", Wea. Forecasting, 13, pp.1093~1104 (1998)
- Jedlovec, G. J, "Quality and control of water vapor winds", Proc. 8th Conf. on Satellite Meteorology and Oceanography, Atlanta, GA, Amer. Meteor. Soc, pp.5~8 (1996)
- Le Marshall, J, "The real-time generation and application of cloud-drift winds in the Australian region", Aust. Met. Mag., 42, pp.89~103 (1993)
- Merrill, R. T., "A report on the recent demonstration of NOAA’s upgraded capability to derive satellite cloud motion winds", Bull. Amer. Meteor. Soc, 72, pp.372~376 (1991)
- Negri, R. G., "A tool to detect inner cloud top dynamics of deep convective system", Proc. 10th Int. Winds Workshop, Tokyo, Japan, pp.56~ (2010)
- Nieman, S. J., "Fully automated cloud-drift winds in NESDIS operations", Bull. Amer. Meteor. Soc, 78, pp.1121~1133 (1997)
- "Product User Manual for “High Resolution Winds” (HRW-PGE09 v3.1)" (2011)[URL]
- Ottenbacher, A, "Low-level cloud motion winds from Meteosat high-resolution visible imagery", Wea. Forecasting, 12, pp.175~184 (1997)
- Oyama, R., "Recent upgrades of and activities for atmospheric motion vectors at JMA", MSC, 2010, 56~
- Saunders R. W, "An improved fast radiative transfer model for assimilation of satellite radiance observations", Quart. J. Royal Meteorol. Soc, 125, pp.1407~1425 (1999)
- Velden, C. S., "Recent innovations in deriving tropospheric winds from meteorological satellites", Bull. Amer. Meteor. Soc, 86, pp.205~223 (2005)
- Yamashita, K., "Observing system experiments of MTSAT-2 rapid scan atmospheric motion vector for TPARC2008 using the JMA operational NWP system", Proc. 10th Int. Winds Workshop, Tokyo, Japan, pp.56~ (2008)