• Atmosphere
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• v.22 no.1
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• pp.1-12
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• 2012

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 ($meso-{\beta}$ and $meso-{\gamma}$ 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 $meso-{\beta}$ 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 ${\times}$ 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.

• Proceedings of the KSRS Conference
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• 2003.11a
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• pp.331-333
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• 2003

KMA successfully began to receive and utilize the GOES-9 GVAR data since May 22nd 2003 when GOES-9 replaced the long-lived GMS-5 for Western Pacific and East Asian region until operation of MTSAT-1R in 2004. To take advantage of improvements of the GOES-9 data over the GMS-5 data, such as the increase of the temporal and spat ial resolution and addition of 3.9${\mu}$m channel, we have improved several algorithms to derive the meteorological products. Here we show two examples of algorithms, sea surface temperature and atmospheric motion vector, and preliminary results of validation of the improved algorithm.

• Korean Journal of Remote Sensing
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• v.37 no.5_1
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• pp.885-901
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• 2021

This paper illustratesthe impact of the temporal gap between satellite images and targetsize in mesoscale atmospheric motion vector (AMV) algorithm. A test has been performed using GEO-KOMPSAT-2A (GK2A) rapid-scan data sets with a temporal gap varying between 2 and 10 minutes and a targetsize between 8×8 and 40×40. Resultsshow the variation of the number of AMVs produced, mean AMV speed, and validation scores as a function of temporal gap and target size. As a results, it was confirmed that the change in the number of vectors and the normalized root-mean squared vector difference (NRMSVD) became more pronounced when smaller targets are used. In addition, it was advantageous to use shorter temporal gap and smaller target size for the AMV calculation in the lower layer, where the average speed is low and the spatio-temporal scale of atmospheric phenomena is small. The temporal gap and the targetsize are closely related to the spatial and temporalscale of the atmospheric circulation to be observed with AMVs. Thus, selecting the target size and temporal gap for an optimum calculation of AMVsrequires considering them. This paper recommendsthat the optimized configuration to be used operationally for the near-real time analysis of mesoscale meteorological phenomena is 4-min temporal gap and 16×16 pixel target size, respectively.

• Atmosphere
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• v.28 no.4
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• pp.443-455
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• 2018

Nowcasting algorithms using weather radar data are mostly based on extrapolating the radar echoes. We estimate the echo motion vectors that are used to extrapolate the echo properly. Therefore, understanding the general characteristics of these motion vectors is important to improve the performance of nowcasting. General characteristics of radar-based motions are analyzed for warm season precipitation over Jeju region. Three-year summer season data (June~August, 2011~2013) from two radars (GSN, SSP) in Jeju are used to obtain echo motion vectors that are retrieved by Variational Echo Tracking (VET) method which is widely used in nowcasting. The highest frequency occurs in precipitation motion toward east-northeast with the speed of $15{\sim}16m\;s^{-1}$ during the warm season. Precipitation system moves faster and eastward in June-July while it moves slower and northeastward in August. The maximum frequency of speed appears in $10{\sim}20m\;s^{-1}$ and $5{\sim}10m\;s^{-1}$ in June~July and August respectively while average speed is about $14{\sim}15m\;s^{-1}$ in June~July and $8m\;s^{-1}$ in August. In addition, the direction of precipitation motion is highly variable in time in August. The speed of motion in Lee side of the island is smaller than that of the windward side.

• Korean Journal of Remote Sensing
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• v.38 no.6_1
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• pp.1069-1080
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• 2022

The tropospheric ozone is a pollutant that causes a great deal of damage to humans and ecosystems worldwide. In the event that ozone moves downwind from its source, a localized problem becomes a regional and global problem. To enhance ozone monitoring efficiency, geostationary satellites with continuous diurnal observations have been developed. The objective of this study is to derive the Tropospheric Ozone Movement Vector (TOMV) by employing continuous observations of tropospheric ozone from geostationary satellites for the first time in the world. In the absence of Geostationary Environmental Monitoring Satellite (GEMS) tropospheric ozone observation data, the GEOS-Chem model calculated values were used as synthetic data. Comparing TOMV with GEOS-Chem, the TOMV algorithm overestimated wind speed, but it correctly calculated wind direction represented by pollution movement. The ozone influx can also be calculated using the calculated ozone movement speed and direction multiplied by the observed ozone concentration. As an alternative to a backward trajectory method, this approach will provide better forecasting and analysis by monitoring tropospheric ozone inflow characteristics on a continuous basis. However, if the boundary of the ozone distribution is unclear, motion detection may not be accurate. In spite of this, the TOMV method may prove useful for monitoring and forecasting pollution based on geostationary environmental satellites in the future.

• Journal of the Korean earth science society
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• v.39 no.4
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• pp.305-316
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• 2018

Satellite-derived sea surface winds (SSWs) and atmospheric motion vectors (AMVs) over the global ocean, particularly including the areas in and around tropical cyclones (TCs), have been provided in a real-time and continuous manner. More and better information is now derived from technologically improved multiple satellite missions and wind retrieving techniques. The status and prospects of key SSW products retrieved from scatterometers, passive microwave radiometers, synthetic aperture radar, and altimeters as well as AMVs derived by tracking features from multiple geostationary satellites are reviewed here. The quality and error characteristics, limitations, and challenges of satellite wind observations described in the literature, which need to be carefully considered to apply the observations for both operational and scientific uses, i.e., assimilation in numerical weather forecasting, are also described. Additionally, on-going efforts toward merging them, particularly for monitoring three-dimensional TC wind fields in a real-time and continuous manner and for providing global profiles of high-quality wind observations with the new mission are introduced. Future research is recommended to develop plans for providing more and better SSW and AMV products in a real-time and continuous manner from existing and new missions.

• Proceedings of the KSRS Conference
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• 2007.10a
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• pp.153-155
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• 2007

Korea Meteorological Administration (KMA) has operationally produced Atmospheric Motion Vector (AMV) from the consecutive MTSAT-1R satellite image dataset. Comparing with radiosonde data, our current AMV scheme shows more than 10 m/s RMSE. Therefore we need to improve continuously its accuracy. Many AMV producers have stated that the bad performance of the Height Assignment (HA) algorithm is the main reason of degrading the accuracy of AMV. The uncertainties in AMV HA can occur in the algorithm itself, used NWP profiles, and the performance of Radiative Transfer Model (RTM) etc. This study introduces currently operated AMV HA schemes and the impacts of NWP profile data and RTM that these schemes use were investigated. Finally we analyzed the relationship between vectors by vector tracking and heights assigned to each vector by using collocated wind profile dataset with radiosonde data. This study is a preliminary work to improve the accuracy of AMV by removing or decreasing the uncertainties in AMV estimation.

• Atmosphere
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• v.27 no.1
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• pp.93-104
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• 2017

The effect of Advanced Microwave Sounding Unit-A (AMSU-A) observations on the short-range forecast in East Asia (EA) was investigated for the Northern Hemispheric (NH) summer and winter months, using the Forecast Sensitivity to Observations (FSO) method. For both periods, the contribution of radiosonde (TEMP) to the EA forecast was largest, followed by AIRCRAFT, AMSU-A, Infrared Atmospheric Sounding Interferometer (IASI), and the atmospheric motion vector of Communication, Ocean and Meteorological Satellite (COMS) or Multi-functional Transport Satellite (MTSAT). The contribution of AMSU-A sensor was largely originated from the NOAA 19, NOAA 18, and MetOp-A (NOAA 19 and 18) satellites in the NH summer (winter). The contribution of AMSU-A sensor on the MetOp-A (NOAA 18 and 19) satellites was large at 00 and 12 UTC (06 and 18 UTC) analysis times, which was associated with the scanning track of four satellites. The MetOp-A provided the radiance data over the Korea Peninsula in the morning (08:00~11:30 LST), which was important to the morning forecast. In the NH summer, the channel 5 observations on MetOp-A, NOAA 18, 19 along the seaside (along the ridge of the subtropical high) increased (decreased) the forecast error slightly (largely). In the NH winter, the channel 8 observations on NOAA 18 (NOAA 15 and MetOp-A) over the Eastern China (Tibetan Plateau) decreased (increased) the forecast error. The FSO provides useful information on the effect of each AMSU-A sensor on the EA forecasts, which leads guidance to better use of AMSU-A observations for EA regional numerical weather prediction.

• 제어로봇시스템학회:학술대회논문집
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• 1991.10a
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• pp.419-424
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• 1991

In this paper, a fuel minimizing closed loop explicit inertial guidance algorithm for the orbit injection of a rocket is developed. In this formulation, the fuel burning rate and magnitude of thrust are assumed constant, and the motion of a rocket is assumed to be subject to the average inverse-square gravity, but with negligible atmospheric effects. The optimum thrust angle for obtaining the given velocity vector in the shortest time with minimizing fuel consumption is first determined, and then the additive thrust angle for targeting the final position vectors is determined by using Pontryagin's Maximum Principle. To establish the real time processing, many algorithms of the onboard guidance software are simplified. Simulations for the explicit guidance algorithm, for the 2nd-stage flight of the N-1 rocket, are carried out. The results show that the guidance algorithm works well in the presence of the maximum .+-.10 % initial velocity and altitude error. The effects of the guidance cycle time is also examined.

• Proceedings of the KSRS Conference
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• 2007.10a
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• pp.146-148
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• 2007

CMDPS (COMS Meteorological Data Processing System) is the operational meteorological products extraction system for data observed from COMS (Communication, Ocean and Meteorological Satellite) meteorological imager. CMDPS baseline products consist of 16 parameters including cloud information, water vapor products, surface information, environmental products and atmospheric motion vector. Additionally, CMDPS includes the function of calibration monitoring, and validation mechanism of the baseline products. The main objective of CMDPS validation module development is near-real time monitoring for the accuracy and reliability of the whole CMDPS products. Also, its long time validation statistics are used for upgrade of CMDPS such as algorithm parameter tuning and retrieval algorithm modification. This paper introduces the preliminary design on CMDPS validation module.