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 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 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 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.

Comparisons of Satellite-Derived Atmospheric Motion Vectors, Rawinsondes, and NOAA Wind Profiler Observations

2009 ◽  
Vol 48 (8) ◽  
pp. 1542-1561 ◽  
Author(s):  
Kristopher M. Bedka ◽  
Christopher S. Velden ◽  
Ralph A. Petersen ◽  
Wayne F. Feltz ◽  
John R. Mecikalski
Keyword(s):  
Boundary Layer ◽  
Prediction Models ◽  
Weather Prediction ◽  
Information Service ◽  
Wind Profiler ◽  
Motion Vectors ◽  
Processing Methods ◽  
Atmospheric Motion ◽  
The Individual ◽  
Atmospheric Motion Vectors

Abstract Geostationary satellite-derived atmospheric motion vectors (AMVs) have been used over several decades in a wide variety of meteorological applications. The ever-increasing horizontal and vertical resolution of numerical weather prediction models puts a greater demand on satellite-derived wind products to monitor flow accurately at smaller scales and higher temporal resolution. The focus of this paper is to evaluate the accuracy and potential applications of a newly developed experimental mesoscale AMV product derived from Geostationary Operational Environmental Satellite (GOES) imagery. The mesoscale AMV product is derived through a variant on processing methods used within the University of Wisconsin—Madison Cooperative Institute for Meteorological Satellite Studies (UW-CIMSS) AMV algorithm and features a significant increase in vector density throughout the troposphere and lower stratosphere over current NOAA/National Environmental Satellite, Data, and Information Service (NESDIS) processing methods for GOES-12 Imager data. The primary objectives of this paper are to 1) highlight applications of experimental GOES mesoscale AMVs toward weather diagnosis and forecasting, 2) compare the coverage and accuracy of mesoscale AMVs with the NOAA/NESDIS operational AMV product, and 3) demonstrate the utility of 6-min NOAA Wind Profiler Network observations for satellite-derived AMV validation. Although the more conservative NOAA/NESDIS AMV product exhibits closer statistical agreement to rawinsonde and wind profiler observations than do the experimental mesoscale AMVs, a comparison of these two products for selected events shows that the mesoscale product better depicts the circulation center of a midlatitude cyclone, boundary layer confluence patterns, and a narrow low-level jet that is well correlated with subsequent severe thunderstorm development. Thus, while the individual experimental mesoscale AMVs may sacrifice some absolute accuracy, they show promise in providing greater temporal and spatial flow detail that can benefit diagnosis of upper-air flow patterns in near–real time. The results also show good agreement between 6-min wind profiler and rawinsonde observations within the 700–200-hPa layer, with larger differences in the stratosphere, near the mean top of the planetary boundary layer, and just above the earth’s surface. Despite these larger differences within select layers, the stability of the difference profile with height builds confidence in the use of 6-min, ∼404-MHz NOAA Wind Profiler Network observations to evaluate and better understand satellite AMV error characteristics.

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 An Adjoint-Based Forecast Impact from Assimilating MISR Winds into the GEOS-5 Data Assimilation and Forecasting System

2017 ◽  
Vol 145 (12) ◽  
pp. 4937-4947 ◽  
Author(s):  
Kevin J. Mueller ◽  
Junjie Liu ◽  
Will McCarty ◽  
Ron Gelaro
Keyword(s):  
Data Assimilation ◽  
Forecast Error ◽  
Motion Vector ◽  
Earth Observing System ◽  
Forecasting System ◽  
Height Assignment ◽  
Sensitivity Method ◽  
Atmospheric Motion Vectors ◽  
Cross Track ◽  
Modern Era

This study examines the benefit of assimilating cloud motion vector (CMV) wind observations obtained from the Multiangle Imaging SpectroRadiometer (MISR) within a Modern-Era Retrospective Analysis for Research and Applications, version 2 (MERRA-2), configuration of the Goddard Earth Observing System-5 (GEOS-5) model data assimilation system (DAS). Available in near–real time (NRT) and with a record dating back to 1999, MISR CMVs boast pole-to-pole coverage and geometric height assignment that is complementary to the suite of atmospheric motion vectors (AMVs) included in the MERRA-2 standard. Experiments spanning September–November of 2014 and March–May of 2015 estimated relative MISR CMV impact on the 24-h forecast error reduction with an adjoint-based forecast sensitivity method. MISR CMV were more consistently beneficial and provided twice as large a mean forecast benefit when larger uncertainties were assigned to the less accurate component of the CMV oriented along the MISR satellite ground track, as opposed to when equal uncertainties were assigned to the eastward and northward components as in previous studies. Assimilating only the cross-track component provided 60% of the benefit of both components. When optimally assimilated, MISR CMV proved broadly beneficial throughout the Earth, with the greatest benefit evident at high latitudes where there is a confluence of more frequent CMV coverage and gaps in coverage from other MERRA-2 wind observations. Globally, MISR represented 1.6% of the total forecast benefit, whereas regionally that percentage was as large as 3.7%.

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

2012 ◽  
Vol 51 (12) ◽  
pp. 2137-2151 ◽  
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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