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

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.

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

Journal of Applied Meteorology and Climatology ◽

10.1175/jamc-d-14-0025.1 ◽

2015 ◽

Vol 54 (1) ◽

pp. 225-242 ◽

Cited By ~ 21

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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2018 Atmospheric Motion Vector (AMV) Intercomparison Study

Remote Sensing ◽

10.3390/rs11192240 ◽

2019 ◽

Vol 11 (19) ◽

pp. 2240 ◽

Cited By ~ 1

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’.

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A Novel Approach for Satellite-Based Turbulence Nowcasting for Aviation

Remote Sensing ◽

10.3390/rs12142255 ◽

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.

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

Remote Sensing ◽

10.3390/rs11172054 ◽

2019 ◽

Vol 11 (17) ◽

pp. 2054 ◽

Cited By ~ 2

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.

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Estimation of Atmospheric Motion Vectors from Kalpana-1 Imagers

Journal of Applied Meteorology and Climatology ◽

10.1175/2009jamc2159.1 ◽

2009 ◽

Vol 48 (11) ◽

pp. 2410-2421 ◽

Cited By ~ 35

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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2018 Atmospheric Motion Vector (AMV) Intercomparison Study

10.20944/preprints201908.0017.v1 ◽

2019 ◽

Cited By ~ 1

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”.

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

Journal of Applied Meteorology and Climatology ◽

10.1175/jamc-d-15-0260.1 ◽

2016 ◽

Vol 55 (10) ◽

pp. 2211-2227 ◽

Cited By ~ 1

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.

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Exploiting Aeolus Level-2B Winds to Better Characterize Atmospheric Motion Vector Bias and Uncertainty

10.5194/amt-2021-277 ◽

2021 ◽

Author(s):

Katherine E. Lukens ◽

Kayo Ide ◽

Kevin Garrett ◽

Hui Liu ◽

David Santek ◽

...

Keyword(s):

Weather Prediction ◽

Motion Vector ◽

European Space Agency ◽

Vertical Wind Shear ◽

The Arctic ◽

Horizontal Line ◽

Polar Regions ◽

Molecular Scattering ◽

Uncertainty Estimates ◽

Atmospheric Motion

Abstract. The need for highly accurate atmospheric wind observations is a high priority in the science community, and in particular numerical weather prediction (NWP). To address this requirement, this study leverages Aeolus wind LIDAR Level-2B data provided by the European Space Agency (ESA) to better characterize atmospheric motion vector (AMV) bias and uncertainty, with the eventual goal of potentially improving AMV algorithms. AMV products from geostationary (GEO) and low-Earth polar orbiting (LEO) satellites are compared with reprocessed Aeolus horizontal line-of-sight (HLOS) global winds observed in August and September 2019. Winds from two of the four Aeolus observing modes are utilized for comparison with AMVs: Rayleigh-clear (derived from the molecular scattering signal) and Mie-cloudy (derived from particle scattering). For the most direct comparison, quality controlled (QC’d) Aeolus winds are collocated with quality controlled AMVs in space and time, and the AMVs are projected onto the Aeolus HLOS direction. Mean collocation differences (MCD) and standard deviation (SD) of those differences (SDCD) are determined from comparisons based on a number of conditions, and their relation to known AMV bias and uncertainty estimates is discussed. GOES-16 and LEO AMV characterizations based on Aeolus winds are described in more detail. Overall, QC’d AMVs correspond well with QC’d Aeolus HLOS wind velocities (HLOSV) for both Rayleigh-clear and Mie-cloudy observing modes, despite remaining biases in Aeolus winds after reprocessing. Comparisons with Aeolus HLOSV are consistent with known AMV bias and uncertainty in the tropics, NH extratropics, and in the Arctic, and at mid- to upper-levels in both clear and cloudy scenes. SH comparisons generally exhibit larger than expected SDCD, which could be attributed to height assignment errors in regions of high winds and enhanced vertical wind shear. GOES-16 water vapor clear-sky AMVs perform best relative to Rayleigh-clear winds, with small MCD (-0.6 m s-1 to 0.1 m s-1) and SDCD (5.4–5.6 m s-1) in the NH and tropics that fall within the accepted range of AMV error values relative to radiosonde winds. Compared to Mie-cloudy winds, AMVs exhibit similar MCD and smaller SDCD (~4.4–4.8 m s-1) throughout the troposphere. In polar regions, Mie-cloudy comparisons have smaller SDCD (5.2 m s-1 in the Arctic, 6.7 m s-1 in the Antarctic) relative to Rayleigh-clear comparisons, which are larger by 1–2 m s-1. The level of agreement between AMVs and Aeolus winds varies per combination of conditions including the Aeolus observing mode coupled with AMV derivation method, geographic region, and height of the collocated winds. It is advised that these stratifications be considered in future comparison studies and impact assessments involving 3D winds. Additional bias corrections to the Aeolus dataset are anticipated to further refine the results.

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Understanding Atmospheric Motion Vector Vertical Representativity Using a Simulation Study and First-Guess Departure Statistics

Journal of Applied Meteorology and Climatology ◽

10.1175/jamc-d-15-0030.1 ◽

2015 ◽

Vol 54 (12) ◽

pp. 2479-2500 ◽

Cited By ~ 9

Author(s):

Peter Lean ◽

Stefano Migliorini ◽

Graeme Kelly

Keyword(s):

Simulation Study ◽

Mesoscale Model ◽

Motion Vector ◽

Computational Cost ◽

Radiative Transfer Model ◽

Transfer Model ◽

Medium Level ◽

Atmospheric Motion ◽

Upper Level ◽

Height Assignment

AbstractAtmospheric motion vectors (AMVs) have been produced for decades and remain an important source of wind information. Many studies have suggested that the traditional interpretation of AMVs as representative of the wind at cloud top is suboptimal and that they are more representative of the winds within the cloud. This paper investigates the vertical representativity of cloudy AMVs using both first-guess departure [observation − background (O − B)] statistics and the simulation-study technique. A state-of-the-art convection-permitting mesoscale model (“UKV”) is used in conjunction with a radiative transfer model and the Nowcasting Satellite Application Facility (NWCSAF) AMV package to produce synthetic AMVs over a 1-month period. The simulated upper-level AMVs suffered from large height-assignment errors uncharacteristic of those in reality; these issues were partially alleviated by using the model cloud top instead of the assigned height. In agreement with previous studies, both the simulated and real AMVs were found to have the closest fit to a layer mean of the model winds with the majority of the layer below the estimated cloud top. However, improvements in the fit between the AMVs and the model were also found by simply lowering the assigned height. A short NWP trial hinted that height reassignment might lead to short-range forecast improvements. The results of this study indicate that the simulation technique was able to match the usefulness of O − B statistics for AMVs associated with low- and medium-level clouds (albeit at a higher computational cost); however, challenges remain in the simulation of upper-level clouds.

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New Methods toward Minimizing the Slow Speed Bias Associated with Atmospheric Motion Vectors

Journal of Applied Meteorology and Climatology ◽

10.1175/jamc-d-11-0234.1 ◽

2012 ◽

Vol 51 (12) ◽

pp. 2137-2151 ◽

Cited By ~ 37

Author(s):

Wayne C. Bresky ◽

Jaime M. Daniels ◽

Andrew A. Bailey ◽

Steven T. Wanzong

Keyword(s):

Motion Vector ◽

Local Motion ◽

Slow Speed ◽

New Approach ◽

Tracking Process ◽

Increased Risk ◽

Atmospheric Motion ◽

Control Set ◽

Height Assignment ◽

Atmospheric Motion Vectors

AbstractComparisons between satellite-derived winds and collocated rawinsonde observations often show a pronounced slow speed bias at mid- and upper levels of the atmosphere. A leading cause of the slow speed bias is the improper assignment of the tracer to a height that is too high in the atmosphere. Height errors alone cannot fully explain the slow bias, however. Another factor influencing the speed bias is the size of the target window used in the tracking step. Tracking with a large target window can cause excessive averaging to occur and a smoothing of the instantaneous wind field. Conversely, if too small a window is specified, there is an increased risk of finding a false match. The authors have developed a new “nested tracking” approach that isolates the dominant local motion within a cloud scene and minimizes the smoothing of the motion estimate. A major advantage of the new approach is the ability to identify which pixels within the cloud scene are contributing to the tracking solution. Knowing which pixels contribute to the dominant motion allows for a more representative height to be derived, thereby directly linking the height assignment to the tracking process, which is an important goal for producers of global atmospheric motion vector (AMV) data. When compared with equivalent rawinsondes, the AMVs derived with the new approach show a considerable improvement in the speed bias and root-mean-square error over a control set of AMVs derived with more-conventional methods.

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