scholarly journals 2018 Atmospheric Motion Vector (AMV) Intercomparison Study

2019 ◽  
Author(s):  
David Santek ◽  
Richard Dworak ◽  
Sharon Nebuda ◽  
Steve Wanzong ◽  
Régis Borde ◽  
...  
Keyword(s):  
Weather Prediction ◽  
Motion Vector ◽  
Optimal Estimation ◽  
Japan Meteorological Agency ◽  
Common Input ◽  
European Organization ◽  
Specific Configuration ◽  
Atmospheric Motion ◽  
Height Assignment ◽  
Intercomparison Study

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/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) JMA AMV algorithm has the best overall performance considering all validation metrics, most likely due to its height assignment: “optimal estimation using observed radiance and NWP wind vertical profile”.

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 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 Lidar-Based Height Correction for the Assimilation of Atmospheric Motion Vectors

2016 ◽  
Vol 55 (10) ◽  
pp. 2211-2227 ◽  
Author(s):  
Kathrin Folger ◽  
Martin Weissmann
Keyword(s):  
Bias Correction ◽  
Prediction Models ◽  
Weather Prediction ◽  
Motion Vector ◽  
Small Subset ◽  
Deep Layers ◽  
Forecasting System ◽  
Atmospheric Motion ◽  
Height Assignment ◽  
Atmospheric Motion Vectors

AbstractThis study uses lidar observations from the polar-orbiting Cloud–Aerosol Lidar and Infrared Pathfinder Satellite Observations (CALIPSO) satellite to correct operational atmospheric motion vector (AMV) pressure heights. This intends to reduce the height assignment error of AMVs for their use in data assimilation. Additionally, AMVs are treated as winds in a vertical layer as proposed by several recent studies. Corrected and uncorrected AMV winds are evaluated using short-term forecasts of the global forecasting system of the German Weather Service. First, a direct lidar-based height reassignment of AMVs with collocated CALIPSO observations is evaluated. Assigning AMV winds from Meteosat-10 to ~120-hPa-deep layers below the lidar cloud top reduces the vector root-mean-square (VRMS) differences of AMVs from Meteosat-10 by 8%–15%. However, such a direct reassignment can only be applied to collocated AMV–CALIPSO observations that compose a comparably small subset of all AMVs. Second, CALIPSO observations are used to derive statistical height bias correction functions for a general height correction of all operational AMVs from Meteosat-10. Such a height bias correction achieves on average about 50% of the reduction of VRMS differences of the direct height reassignment. Results for other satellites are more ambiguous but still encouraging. Given that such a height bias correction can be applied to all AMVs from a geostationary satellite, the method exhibits a promising approach for the assimilation of AMVs in numerical weather prediction models in the future.

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.

scholarly journals Height Correction of Atmospheric Motion Vectors Using Airborne Lidar Observations

2013 ◽  
Vol 52 (8) ◽  
pp. 1868-1877 ◽  
Author(s):  
Martin Weissmann ◽  
Kathrin Folger ◽  
Heiner Lange
Keyword(s):  
North Pacific Ocean ◽  
Weather Prediction ◽  
Airborne Lidar ◽  
Discrete Level ◽  
Motion Vectors ◽  
Wind Speeds ◽  
Atmospheric Motion ◽  
Height Assignment ◽  
Atmospheric Motion Vectors ◽  
Lidar Observations

AbstractUncertainties in the height assignment of atmospheric motion vectors (AMVs) are the main contributor to the total AMV wind error, and these uncertainties introduce errors that can be horizontally correlated over several hundred kilometers. As a consequence, only a small fraction of the available AMVs are currently used in numerical weather prediction systems. For this reason, alternative approaches for the height assignment of AMVs are investigated in this study: 1) using collocated airborne lidar observations and 2) treating AMVs as layer winds instead of winds at a discrete level. Airborne lidar observations from a field campaign in the western North Pacific Ocean region are used to demonstrate the potential of improving AMV heights in an experimental framework. On average, AMV wind errors are reduced by 10%–15% when AMV winds are assigned to a 100–150-hPa-deep layer beneath the cloud top derived from nearby lidar observations. In addition, the lidar–AMV height correction is expected to reduce the correlation of AMV errors as lidars provide independent cloud height information. This suggests that satellite lidars may be a valuable source of information for the AMV height assignment in the future. Furthermore, AMVs are compared with dropsonde and radiosonde winds averaged over vertical layers of different depth to investigate the optimal height assignment for AMVs in data assimilation. Consistent with previous studies, it is shown that AMV winds better match sounding winds vertically averaged over ~100 hPa than sounding winds at a discrete level. The comparison with deeper layers further reduces the RMS difference but introduces systematic differences of wind speeds.

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.

scholarly journals Height Correction of Atmospheric Motion Vectors Using Satellite Lidar Observations from CALIPSO

2014 ◽  
Vol 53 (7) ◽  
pp. 1809-1819 ◽  
Author(s):  
Kathrin Folger ◽  
Martin Weissmann
Keyword(s):  
Prediction Models ◽  
Initial Conditions ◽  
Weather Prediction ◽  
Current Data ◽  
Motion Vectors ◽  
Deep Layers ◽  
Atmospheric Motion ◽  
Height Assignment ◽  
Atmospheric Motion Vectors ◽  
Lidar Observations

AbstractAtmospheric motion vectors (AMVs) provide valuable wind information for the initial conditions of numerical weather prediction models, but height-assignment issues and horizontal error correlations require a rigid thinning of the available AMVs in current data assimilation systems. The aim of this study is to investigate the feasibility of correcting the pressure heights of operational AMVs from the geostationary satellites Meteosat-9 and Meteosat-10 with cloud-top heights derived from lidar observations by the polar-orbiting Cloud–Aerosol Lidar and Infrared Pathfinder Satellite Observations (CALIPSO) satellite. The study shows that the wind error of AMVs above 700 hPa is reduced by 12%–17% when AMV winds are assigned to 120-hPa-deep layers below the lidar cloud tops. This result demonstrates the potential of lidar cloud observations for the improvement of the AMV height assignment. In addition, the lidar correction reduces the “slow” bias of current upper-level AMVs and is expected to reduce the horizontal correlation of AMV errors.

scholarly journals Characterizing AMV Height-Assignment Error by Comparing Best-Fit Pressure Statistics from the Met Office and ECMWF Data Assimilation Systems

2015 ◽  
Vol 54 (1) ◽  
pp. 225-242 ◽  
Author(s):  
Kirsti Salonen ◽  
James Cotton ◽  
Niels Bormann ◽  
Mary Forsythe
Keyword(s):  
Data Assimilation ◽  
Motion Vector ◽  
Assignment Method ◽  
Uncertainty Estimates ◽  
Atmospheric Motion ◽  
The Difference ◽  
Height Assignment ◽  
Best Fit ◽  
Assignment Error

AbstractTo ensure realistic use of atmospheric motion vector (AMV) observations in data assimilation, the error characteristics of the observation type need to be known and carefully taken into account. Assigning a height to the tracked feature is one of the most significant error sources for AMV observations. In this article, the characteristics of the AMV height-assignment error are studied by comparing model best-fit pressure statistics between the Met Office and ECMWF data assimilation systems. The aim is to provide detailed uncertainty estimates for the assigned pressure and to demonstrate that the best-fit pressure enables reliable estimation of the uncertainties in the AMV height assignment. Typical values for the standard deviation of the difference between the assigned pressure and the best-fit pressure are 50–80 hPa at high levels, 115–165 hPa at midlevels, and 60–125 hPa at low levels, depending on satellite, channel, and height-assignment method. Observed minus best-fit pressure biases are mostly within the range of ±50 hPa. The results are very similar for the Met Office and ECMWF systems, suggesting that the pressure differences are not strongly dependent on the data assimilation system. Furthermore, the findings are in good agreement with the expected characteristics of the height-assignment methods and quality of the observations. Thus, best-fit pressure statistics give reliable information about the uncertainties in the AMV height assignment.

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