scholarly journals Assessment of FY‐2G Atmospheric Motion Vector Data and Assimilating Impacts on Typhoon Forecasts

2021 ◽  
Author(s):  
Jiahao Liang ◽  
Keyi Chen ◽  
Zhipeng Xian
Keyword(s):  
Motion Vector ◽  
Vector Data ◽  
Atmospheric Motion

scholarly journals 2018 Atmospheric Motion Vector (AMV) Intercomparison Study

2019 ◽  
Vol 11 (19) ◽  
pp. 2240 ◽  
Author(s):  
David Santek ◽  
Richard Dworak ◽  
Sharon Nebuda ◽  
Steve Wanzong ◽  
Régis Borde ◽  
...  
Keyword(s):  
Weather Prediction ◽  
Motion Vector ◽  
Estimation Method ◽  
Japan Meteorological Agency ◽  
Radiative Transfer Model ◽  
Transfer Model ◽  
European Organization ◽  
Specific Configuration ◽  
Atmospheric Motion ◽  
Height Assignment

Atmospheric Motion Vectors (AMVs) calculated by six different institutions (Brazil Center for Weather Prediction and Climate Studies/CPTEC/INPE, European Organization for the Exploitation of Meteorological Satellites/EUMETSAT, Japan Meteorological Agency/JMA, Korea Meteorological Administration/KMA, Unites States National Oceanic and Atmospheric Administration/NOAA, and the Satellite Application Facility on Support to Nowcasting and Very short range forecasting/NWCSAF) with JMA’s Himawari-8 satellite data and other common input data are here compared. The comparison is based on two different AMV input datasets, calculated with two different image triplets for 21 July 2016, and the use of a prescribed and a specific configuration. The main results of the study are summarized as follows: (1) the differences in the AMV datasets depend very much on the ‘AMV height assignment’ used and much less on the use of a prescribed or specific configuration; (2) the use of the ‘Common Quality Indicator (CQI)’ has a quantified skill in filtering collocated AMVs for an improved statistical agreement between centers; (3) Among the six AMV operational algorithms verified by this AMV Intercomparison, JMA AMV algorithm has the best overall performance considering all validation metrics, mainly due to its new height assignment method: ‘Optimal estimation method considering the observed infrared radiances, the vertical profile of the Numerical Weather Prediction wind, and the estimated brightness temperature using a radiative transfer model’.

scholarly journals A Novel Approach for Satellite-Based Turbulence Nowcasting for Aviation

2020 ◽  
Vol 12 (14) ◽  
pp. 2255
Author(s):  
Axel Barleben ◽  
Stéphane Haussler ◽  
Richard Müller ◽  
Matthias Jerg
Keyword(s):  
Weather Prediction ◽  
Motion Vector ◽  
The Novel ◽  
Air Turbulence ◽  
Novel Approach ◽  
Atmospheric Motion ◽  
Nwp Model ◽  
Height Assignment ◽  
Horizontal Grid ◽  
Assignment Scheme

The predictability of aviation turbulence is influenced by energy-intensive flow patterns that are significantly smaller than the horizontal grid scale of current numerical weather prediction (NWP) models. The parameterization of these subgrid scale (SGS) processes is possible by means of an additional prognostic equation for the temporal change of turbulence kinetic energy (TKE), whereby scale transfer terms are used. This turbulence scheme has been applied operationally for 5 years in the NWP model ICON (Icosahedral Nonhydrostatic). The most important of the source terms parameterizes the Kelvin–Helmholtz instability, better known as clear air turbulence. This shear term was subjected to a nowcasting technique, is calculated with satellite data, and shifted forward in time using motion based on optical flow estimates and atmospheric motion vector (AMV). The nowcasts include turbulence altitude as determined by an adapted height assignment scheme presented here. The case studies illustrate that the novel approach for satellite-based turbulence nowcasting is a supplement to the NWP models.

scholarly journals Sensitivity of the Track and Intensity Forecasts of Typhoon Megi (2010) to Satellite-Derived Atmospheric Motion Vectors with the Ensemble Kalman Filter

2012 ◽  
Vol 29 (12) ◽  
pp. 1794-1810 ◽  
Author(s):  
Chanh Q. Kieu ◽  
Nguyen Minh Truong ◽  
Hoang Thi Mai ◽  
Thanh Ngo-Duc
Keyword(s):  
Kalman Filter ◽  
Motion Vector ◽  
The Philippines ◽  
Intensity Change ◽  
Track Forecast ◽  
Recent Effort ◽  
Low Level ◽  
Sharp Turn ◽  
Atmospheric Motion ◽  
Upper Level

Abstract In this study, sensitivities of the track and intensity forecasts of Typhoon Megi (2010) to the Cooperative Institute for Meteorological Satellite Studies (CIMSS) University of Wisconsin satellite atmospheric motion vector (AMV) dataset are examined. Assimilation of the CIMSS AMV dataset using the local ensemble transform Kalman filter implemented in the Weather Research and Forecasting model shows that the AMV data can significantly improve the track forecast of Typhoon Megi, especially the sharp turn from west-northwest to north after crossing the Philippines. By broadening the western Pacific subtropical high to the west, the AMV data can help reduce the eastward bias of the track, thus steering the storm away inimical shear environment and facilitating its subsequent development. Further sensitivity experiments with separated assimilation of the low- to midlevel (800–300 hPa) and upper-level (300–100 hPa) AMV winds reveal that, despite the sparse distribution of the low-level AMV winds with most of the data points located in the periphery of Megi’s main circulation, the track forecasts tend to be more sensitive to the low-level than to the upper-level wind observations. This indicates that the far-field low-level observations can improve the large-scale environmental flow that storms are to move in, giving rise to a better representation of the steering flow and subsequent intensity change. While much of the recent effort in tropical cyclone research focuses on inner-core observations to improve the intensity forecast, the results in this study show that the peripheral observations outside the storm center could contribute considerably to the intensity and track forecasts and deserve attention for better typhoon forecast skills.

scholarly journals SECURE VIDEO CODED SYSTEM MODEL

2016 ◽  
Vol 42 (1) ◽  
pp. 5-9
Author(s):  
Tariq Fadil
Keyword(s):  
Video Compression ◽  
Orthogonal Frequency Division Multiplexing ◽  
Motion Vector ◽  
Video Signal ◽  
Video Data ◽  
Video Codec ◽  
System Model ◽  
Vector Data ◽  
Signal Sample ◽  
Performance Results

In this paper, overall system model, shown in Figure (1), of video compression-encryption-transmitter/decompression-dencryption-receiver was designed and implemented. The modified video codec system has used and in addition to compression/decompression, theencryption/decryption video signal by using chaotic neural network (CNN) algorithm was done. Both of quantized vector data and motion vector data have been encrypted by CNN. The compressed and encrypted video data stream has been sent to receiver by using orthogonal frequency division multiplexing (OFDM) modulation technique. The system model was designed according to video signal sample size of 176 × 144 (QCIFstandard format) with rate of 30 frames per second. Overall system model integrates and operates successfully with acceptable performance results.

scholarly journals Development and Intercomparison Study of an Atmospheric Motion Vector Retrieval Algorithm for GEO-KOMPSAT-2A

2019 ◽  
Vol 11 (17) ◽  
pp. 2054 ◽  
Author(s):  
Soo Min Oh ◽  
Régis Borde ◽  
Manuel Carranza ◽  
In-Chul Shin
Keyword(s):  
Pearson Correlation ◽  
Weather Prediction ◽  
Motion Vector ◽  
Inversion Layer ◽  
Retrieval Algorithm ◽  
Total Contribution ◽  
Sensitivity Tests ◽  
Vector Difference ◽  
Atmospheric Motion ◽  
Height Assignment

We derived an atmospheric motion vector (AMV) algorithm for the Geostationary Korea Multipurpose Satellite (GEO-KOMPSAT-2A; GK-2A) launched on 4 December 2018, using the Advanced Himawari Imager (AHI) onboard Himawari-8, which is very similar to the Advanced Meteorological Imager onboard GK-2A. This study clearly describes the main steps in our algorithm and optimizes it for the target box size and height assignment methods by comparing AMVs with numerical weather prediction (NWP) and rawinsonde profiles for July 2016 and January 2017. Target box size sensitivity tests were performed from 8 × 8 to 48 × 48 pixels for three infrared channels and from 16 × 16 to 96 × 96 pixels for one visible channel. The results show that the smaller box increases the speed, whereas the larger one slows the speed without quality control. The best target box sizes were found to be 16 × 16 for CH07, 08, and 13, and 48 × 48 pixels for CH03. Height assignment sensitivity tests were performed for several methods, such as the cross-correlation coefficient (CCC), equivalent blackbody temperature (EBBT), infrared/water vapor (IR/WV) intercept, and CO2 slicing methods for a cloudy target as well as normalized total contribution (NTC) and normalized total cumulative contribution (NTCC) for a clear-air target. For a cloudy target, the CCC method is influenced by the quality of the cloud’s top pressure. Better results were found when using EBBT and IR/WV intercept methods together rather than individually. Furthermore, CO2 slicing had the best statistics. For a clear-air target, the combined use of NTC and NTCC had the best statistics. Additionally, the mean vector difference, root-mean-square (RMS) vector difference, bias, and RMS error (RMSE) between GK-2A AMVs and NWP or rawinsonde were smaller by approximately 18.2% on average than in the case of the Communication, Ocean and Meteorology Satellite (COMS) AMVs. In addition, we verified the similarity between GK-2A and Meteosat Third Generation (MTG) AMVs using the AHI of Himawari-8 from 21 July 2016. This similarity can provide evidence that the GK-2A algorithm works properly because the GK-2A AMV algorithm borrows many methods of the MTG AMV algorithm for geostationary data and inversion layer corrections. The Pearson correlation coefficients in the speed, direction, and height of the prescribed GK-2A and MTG AMVs were larger than 0.97, and the corresponding bias/RMSE were0.07/2.19 m/s, 0.21/14.8°, and 2.61/62.9 hPa, respectively, considering common quality indicator with forecast (CQIF) > 80.

scholarly journals Winds of Change for Future Operational AMV at EUMETSAT

2019 ◽  
Vol 11 (18) ◽  
pp. 2111 ◽  
Author(s):  
Borde ◽  
Carranza ◽  
Hautecoeur ◽  
Barbieux
Keyword(s):  
Land Surface ◽  
Motion Vector ◽  
European Organization ◽  
Current Contribution ◽  
The World ◽  
Atmospheric Motion ◽  
Wind Profiles ◽  
New Generation ◽  
Geostationary Ring

EUMETSAT, the European Organization for the Exploitation of Meteorological Satellites, is one of the key contributors to global atmospheric motion vector (AMV) production around the world. Its current contribution includes geostationary satellites at 0.0 and 41.5 degrees east, and several products extracted from the Metop low-orbit satellites. These last ones mainly cover high-latitude regions completing the observations from the geostationary ring. In the upcoming years, EUMETSAT will launch a new generation of geostationary and low-orbit satellites. The imager instruments Flexible Combined Imager (FCI) and METImage will take over the nominal AMV production at EUMETSAT around 2022 and 2024. The enhanced characteristics of these new-generation instruments are expected to increase AMV production and to improve the quality of the products. This paper presents an overview of the current EUMETSAT AMV operational production, together with a roadmap of the preparation activities for the new generation of satellites. The characteristics of the upcoming AMV products are described and compared to the current operational AMV products. This paper also presents a recent investigation into AMV extraction using the Sentinel-3 Sea and Land Surface Temperature Radiometer (SLSTR) instrument, as well as the retrieval of wind profiles from infrared sounders.

scholarly journals Identifying the Uncertainty in Determining Satellite-Derived Atmospheric Motion Vector Height Attribution

2009 ◽  
Vol 48 (3) ◽  
pp. 450-463 ◽  
Author(s):  
Christopher S. Velden ◽  
Kristopher M. Bedka
Keyword(s):  
Great Plains ◽  
Motion Vector ◽  
Vertical Wind Shear ◽  
Careful Analysis ◽  
Department Of Energy ◽  
Dominant Factor ◽  
The North ◽  
Assignment Method ◽  
Atmospheric Motion ◽  
Height Assignment

Abstract This study investigates the assignment of pressure heights to satellite-derived atmospheric motion vectors (AMVs), commonly known as cloud-drift and water vapor–motion winds. Large volumes of multispectral AMV datasets are compared with collocated rawinsonde wind profiles collected by the U.S. Department of Energy Atmospheric Radiation Measurement Program at three geographically disparate sites: the southern Great Plains, the North Slope of Alaska, and the tropical western Pacific Ocean. From a careful analysis of these comparisons, the authors estimate that mean AMV observation errors are ∼5–5.5 m s−1 and that vector height assignment is the dominant factor in AMV uncertainty, contributing up to 70% of the error. These comparisons also reveal that in most cases the RMS differences between matched AMVs and rawinsonde wind values are minimized if the rawinsonde values are averaged over specified layers. In other words, on average, the AMV values better correlate to a motion over a mean tropospheric layer rather than to a traditionally assigned discrete level. The height assignment behavioral characteristics are specifically identified according to AMV height (high cloud vs low cloud), type (spectral bands; clear vs cloudy), geolocation, height assignment method, and amount of environmental vertical wind shear present. The findings have potentially important implications for data assimilation of AMVs, and these are discussed.

Influence of Assimilating Satellite-Derived Atmospheric Motion Vector Observations on Numerical Analyses and Forecasts of Tropical Cyclone Track and Intensity

2014 ◽  
Vol 142 (1) ◽  
pp. 49-71 ◽  
Author(s):  
Ting-Chi Wu ◽  
Hui Liu ◽  
Sharanya J. Majumdar ◽  
Christopher S. Velden ◽  
Jeffrey L. Anderson
Keyword(s):  
Motion Vector ◽  
Optimal Strategies ◽  
Weather Research And Forecasting ◽  
Ensemble Forecasts ◽  
University Of Wisconsin ◽  
Full Resolution ◽  
Rapid Scan ◽  
Order Of Magnitude ◽  
Atmospheric Motion ◽  
Atmospheric Motion Vectors

Abstract The influence of assimilating enhanced atmospheric motion vectors (AMVs) on mesoscale analyses and forecasts of tropical cyclones (TC) is investigated. AMVs from the geostationary Multifunctional Transport Satellite (MTSAT) are processed by the Cooperative Institute for Meteorological Satellite Studies (CIMSS, University of Wisconsin–Madison) for the duration of Typhoon Sinlaku (2008), which included a rapid intensification phase and a slow, meandering track. The ensemble Kalman filter and the Weather Research and Forecasting Model are utilized within the Data Assimilation Research Testbed. In addition to conventional observations, three different groups of AMVs are assimilated in parallel experiments: CTL, the same dataset assimilated in the NCEP operational analysis; CIMSS(h), hourly datasets processed by CIMSS; and CIMSS(h+RS), the dataset including AMVs from the rapid-scan mode. With an order of magnitude more AMV data assimilated, the CIMSS(h) analyses exhibit a superior track, intensity, and structure to CTL analyses. The corresponding 3-day ensemble forecasts initialized with CIMSS(h) yield smaller track and intensity errors than those initialized with CTL. During the period when rapid-scan AMVs are available, the CIMSS(h+RS) analyses offer additional modifications to the TC and its environment. In contrast to many members in the ensemble forecasts initialized from the CTL and CIMSS(h) analyses that predict an erroneous landfall in China, the CIMSS(h+RS) members capture recurvature, albeit prematurely. The results demonstrate the promise of assimilating enhanced AMV data into regional TC models. Further studies to identify optimal strategies for assimilating integrated full-resolution multivariate data from satellites are under way.

scholarly journals Estimation of Atmospheric Motion Vectors from Kalpana-1 Imagers

2009 ◽  
Vol 48 (11) ◽  
pp. 2410-2421 ◽  
Author(s):  
C. M. Kishtawal ◽  
S. K. Deb ◽  
P. K. Pal ◽  
P. C. Joshi
Keyword(s):  
Water Vapor ◽  
Motion Vector ◽  
Satellite System ◽  
Radiosonde Data ◽  
Motion Vectors ◽  
Vector Difference ◽  
Atmospheric Motion ◽  
The Difference ◽  
Height Assignment ◽  
Atmospheric Motion Vectors

Abstract The estimation of atmospheric motion vectors from infrared and water vapor channels on the geostationary operational Indian National Satellite System Kalpana-1 has been attempted here. An empirical height assignment technique based on a genetic algorithm is used to determine the height of cloud and water vapor tracers. The cloud-motion-vector (CMV) winds at high and midlevels and water vapor winds (WVW) derived from Kalpana-1 show a very close resemblance to the corresponding Meteosat-7 winds derived at the European Organisation for the Exploitation of Meteorological Satellites when both are compared separately with radiosonde data. The 3-month mean vector difference (MVD) of high- and midlevel CMV and WVW winds derived from Kalpana-1 is very close to that of Meteosat-7 winds, when both are compared with radiosonde. When comparing with radiosonde, the low-level CMVs from Kalpana-1 have a higher MVD value than that of Meteosat-7. This may be due to the difference in spatial resolutions of Kalpana-1 and Meteosat-7.

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