scholarly journals Assessment of a new quality control technique in the retrieval of atmospheric motion vectors

2013 ◽  
Vol 22 (2) ◽  
pp. 178-186 ◽  
Author(s):  
S. K. Deb ◽  
C. M. Kishtawal ◽  
Inderpreet Kaur ◽  
P. K. Pal ◽  
A. S. Kiran Kumar
Keyword(s):  
Quality Control ◽  
Control Technique ◽  
Motion Vectors ◽  
Atmospheric Motion ◽  
Quality Control Technique ◽  
Atmospheric Motion Vectors

scholarly journals Assimilation of High-Resolution Satellite-Derived Atmospheric Motion Vectors: Impact on HWRF Forecasts of Tropical Cyclone Track and Intensity

2017 ◽  
Vol 145 (3) ◽  
pp. 1107-1125 ◽  
Author(s):  
Christopher Velden ◽  
William E. Lewis ◽  
Wayne Bresky ◽  
David Stettner ◽  
Jaime Daniels ◽  
...  
Keyword(s):  
Quality Control ◽  
High Resolution ◽  
Tropical Cyclone ◽  
Cyclone Track ◽  
Tropical Cyclone Track ◽  
Motion Vectors ◽  
Atmospheric Motion ◽  
Atmospheric Motion Vectors ◽  
The Impact ◽  
Atlantic Tropical Cyclone

It is well known that global numerical model analyses and forecasts benefit from the routine assimilation of atmospheric motion vectors (AMVs) derived from meteorological satellites. Recent studies have also shown that the assimilation of enhanced (spatial and temporal) AMVs can benefit research-mode regional model forecasts of tropical cyclone track and intensity. In this study, the impact of direct assimilation of enhanced (higher resolution) AMV datasets in the NCEP operational Hurricane Weather Research and Forecasting Model (HWRF) system is investigated. Forecasts of Atlantic tropical cyclone track and intensity are examined for impact by inclusion of enhanced AMVs via direct data assimilation. Experiments are conducted for AMVs derived using two methodologies (“HERITAGE” and “GOES-R”), and also for varying levels of quality control in order to assess and inform the optimization of the AMV assimilation process. Results are presented for three selected Atlantic tropical cyclone events and compared to Control forecasts without the enhanced AMVs as well as the corresponding operational HWRF forecasts. The findings indicate that the direct assimilation of high-resolution AMVs has an overall modest positive impact on HWRF forecasts, but the impact magnitudes are dependent on the 1) availability of rapid scan imagery used to produce the AMVs, 2) AMV derivation approach, 3) level of quality control employed in the assimilation, and 4) vortex initialization procedure (including the degree to which unbalanced states are allowed to enter the model analyses).

scholarly journals The Sentinel-3 SLSTR Atmospheric Motion Vectors Product at EUMETSAT

2021 ◽  
Vol 13 (9) ◽  
pp. 1702
Author(s):  
Kévin Barbieux ◽  
Olivier Hautecoeur ◽  
Maurizio De Bartolomei ◽  
Manuel Carranza ◽  
Régis Borde
Keyword(s):  
Land Surface ◽  
Weather Prediction ◽  
Forecast Model ◽  
Low Earth Orbit ◽  
Reference Image ◽  
Motion Vectors ◽  
Infrared Imager ◽  
Atmospheric Motion ◽  
Atmospheric Motion Vectors ◽  
Leo Satellite

Atmospheric Motion Vectors (AMVs) are an important input to many Numerical Weather Prediction (NWP) models. EUMETSAT derives AMVs from several of its orbiting satellites, including the geostationary satellites (Meteosat), and its Low-Earth Orbit (LEO) satellites. The algorithm extracting the AMVs uses pairs or triplets of images, and tracks the motion of clouds or water vapour features from one image to another. Currently, EUMETSAT LEO satellite AMVs are retrieved from georeferenced images from the Advanced Very-High-Resolution Radiometer (AVHRR) on board the Metop satellites. EUMETSAT is currently preparing the operational release of an AMV product from the Sea and Land Surface Temperature Radiometer (SLSTR) on board the Sentinel-3 satellites. The main innovation in the processing, compared with AVHRR AMVs, lies in the co-registration of pairs of images: the images are first projected on an equal-area grid, before applying the AMV extraction algorithm. This approach has multiple advantages. First, individual pixels represent areas of equal sizes, which is crucial to ensure that the tracking is consistent throughout the processed image, and from one image to another. Second, this allows features that would otherwise leave the frame of the reference image to be tracked, thereby allowing more AMVs to be derived. Third, the same framework could be used for every LEO satellite, allowing an overall consistency of EUMETSAT AMV products. In this work, we present the results of this method for SLSTR by comparing the AMVs to the forecast model. We validate our results against AMVs currently derived from AVHRR and the Spinning Enhanced Visible and InfraRed Imager (SEVIRI). The release of the operational SLSTR AMV product is expected in 2022.

Effect of Assimilating Himawari-8 Atmospheric Motion Vectors on Forecast Errors over East Asia

2018 ◽  
Vol 35 (9) ◽  
pp. 1737-1752 ◽  
Author(s):  
Dae-Hui Kim ◽  
Hyun Mee Kim
Keyword(s):  
East Asia ◽  
Geopotential Height ◽  
Forecast Error ◽  
Experimental Period ◽  
Energy Norm ◽  
Observation System ◽  
Forecast Errors ◽  
Motion Vectors ◽  
Atmospheric Motion ◽  
Atmospheric Motion Vectors

AbstractIn this study, the effect of assimilating Himawari-8 (HIMA-8) atmospheric motion vectors (AMVs) on forecast errors in East Asia is evaluated using observation system experiments based on the Weather Research and Forecasting Model and three-dimensional variational data assimilation system. The experimental period is from 1 August to 30 September 2015, during which both HIMA-8 and Multifunctional Transport Satellite-2 (MTSAT-2) AMVs exist. The energy-norm forecast error based on the analysis of each experiment as reference was reduced more by replacing MTSAT-2 AMVs with HIMA-8 AMVs than by adding HIMA-8 AMVs to the MTSAT-2 AMVs. When the HIMA-8 AMVs replaced or were added to MTSAT-2 AMVs, the observation impact was reduced, which implies the analysis–forecast system was improved by assimilating HIMA-8 AMVs. The root-mean-square error (RMSE) of the 500-hPa geopotential height forecasts based on the analysis of each experiment decreases more effectively when the region lacking in upper-air wind observations is reduced by assimilating both MTSAT-2 and HIMA-8 AMVs. When the upper-air radiosonde (SOUND) observations are used as reference, assimilating more HIMA-8 AMVs decreases the forecast error. Based on various measures, the assimilation of HIMA-8 AMVs has a positive effect on the reduction of forecast errors. The effects on the energy-norm forecast error and the RMSE based on SOUND observations are greater when HIMA-8 AMVs replaced MTSAT-2 AMVs. However, the effects on the RMSE of the 500-hPa geopotential height forecasts are greater when both HIMA-8 and MTSAT-2 AMVs were assimilated, which implies potential benefits of assimilating AMVs from several satellites for forecasts over East Asia depending on the choice of measurement.

scholarly journals Impact of Four-Dimensional Variational Data Assimilation of Atmospheric Motion Vectors on Tropical Cyclone Track Forecasts

2006 ◽  
Vol 21 (4) ◽  
pp. 663-669 ◽  
Author(s):  
Dongliang Wang ◽  
Xudong Liang ◽  
Yihong Duan ◽  
Johnny C. L. Chan
Keyword(s):  
Tropical Cyclone ◽  
Data Assimilation ◽  
Mesoscale Model ◽  
State University ◽  
Motion Vectors ◽  
Geostationary Meteorological Satellite ◽  
Atmospheric Motion ◽  
Atmospheric Motion Vectors ◽  
Data Assimilation Technique ◽  
The Impact

Abstract The fifth-generation Pennsylvania State University–National Center for Atmospheric Research nonhydrostatic Mesoscale Model is employed to evaluate the impact of the Geostationary Meteorological Satellite-5 water vapor and infrared atmospheric motion vectors (AMVs), incorporated with the four-dimensional variational (4DVAR) data assimilation technique, on tropical cyclone (TC) track predictions. Twenty-two cases from eight different TCs over the western North Pacific in 2002 have been examined. The 4DVAR assimilation of these satellite-derived wind observations leads to appreciable improvements in the track forecasts, with average reductions in track error of ∼5% at 12 h, 12% at 24 h, 10% at 36 h, and 7% at 48 h. Preliminary results suggest that the improvement depends on the quantity of the AMV data available for assimilation.

Impact of Kalpana-1 retrieved atmospheric motion vectors on mesoscale model forecast during summer monsoon 2011

2014 ◽  
Vol 120 (3-4) ◽  
pp. 587-599 ◽  
Author(s):  
Inderpreet Kaur ◽  
Prashant Kumar ◽  
S. K. Deb ◽  
C. M. Kishtawal ◽  
P. K. Pal ◽  
...  
Keyword(s):  
Summer Monsoon ◽  
Mesoscale Model ◽  
Model Forecast ◽  
Motion Vectors ◽  
Atmospheric Motion ◽  
Atmospheric Motion Vectors

scholarly journals Development of Enhanced Vortex-Scale Atmospheric Motion Vectors for Hurricane Applications

2019 ◽  
Vol 11 (17) ◽  
pp. 1981 ◽  
Author(s):  
David Stettner ◽  
Christopher Velden ◽  
Robert Rabin ◽  
Steve Wanzong ◽  
Jaime Daniels ◽  
...  
Keyword(s):  
Weather Prediction ◽  
Global Scale ◽  
Motion Vectors ◽  
Weather Events ◽  
Convective Scale ◽  
Satellite Sensors ◽  
Atmospheric Motion ◽  
Scanning Strategies ◽  
New Generation ◽  
Atmospheric Motion Vectors

Atmospheric motion vectors (AMVs) derived from geostationary meteorological satellites have long stood as an important observational contributor to analyses of global-scale tropospheric wind patterns. This paradigm is evolving as numerical weather prediction (NWP) models and associated data assimilation systems are at the point of trying to better resolve finer scales. Understanding the physical processes that govern convectively-driven weather systems is usually hindered by a lack of observations on the scales necessary to adequately describe these events. Fortunately, satellite sensors and associated scanning strategies have improved and are now able to resolve convective-scale flow fields. Coupled with the increased availability of computing capacity and more sophisticated algorithms to track cloud motions, we are now poised to investigate the development and application of AMVs to convective-scale weather events. Our study explores this frontier using new-generation GOES-R Series imagery with a focus on hurricane applications. A proposed procedure for processing enhanced AMV datasets derived from multispectral geostationary satellite imagery for hurricane-scale analyses is described. We focus on the use of the recently available GOES-16 mesoscale domain sector rapid-scan (1-min) imagery, and emerging methods to optimally extract wind estimates (atmospheric motion vectors (AMVs)) from close-in-time sequences. It is shown that AMV datasets can be generated on spatiotemporal scales not only useful for global applications, but for mesoscale applications such as hurricanes as well.

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