scholarly journals First results from the generation and assimilation of 10-minute atmospheric motion vectors in the Australian region

2016 ◽  
Vol 66 (1) ◽  
pp. 12
Author(s):  
John Le Marshall ◽  
Yi Xiao ◽  
David Howard ◽  
Chris Tingwell ◽  
Jeff Freeman ◽  
...  
Keyword(s):  
Satellite Images ◽  
Forecast Model ◽  
High Temporal Resolution ◽  
Motion Vectors ◽  
Minute Interval ◽  
First Results ◽  
Atmospheric Motion ◽  
Atmospheric Motion Vectors ◽  
Regional Forecast

Ten-minute interval infrared and visible satellite images, available from MTSAR-1R (Himawari-6) for a limited period over and around Australia, have been used to generate atmospheric motion vectors. These vectors—which were available every 10 minutes—have been quality controlled, error characterised and assimilated into the Australian Bureau of Meteorology's next generation operational regional forecast model as part of the new operational database. Results from this study indicate that this high temporal resolution imagery has the ability to produce high spatial and temporal density atmospheric motion vectors, and the quality of these vectors is such that they have the potential to improve numerical analysis and prognosis.

scholarly journals Himawari-8 Atmospheric Motion Vectors - Operational Generation and Assimilation

2017 ◽  
Vol 67 (1) ◽  
pp. 12
Author(s):  
John Le Marshall ◽  
David Howard ◽  
Yi Xiao ◽  
Jamie Daniels ◽  
Steve Wanzong ◽  
...  
Keyword(s):  
Weather Prediction ◽  
Forecast Model ◽  
High Temporal Resolution ◽  
Motion Vectors ◽  
Atmospheric Motion ◽  
Nwp Model ◽  
Effective Use ◽  
New Generation ◽  
Atmospheric Motion Vectors ◽  
Resolution Data

In October 2014 the Japanese Meteorological Agency (JMA) launched the new generation geostationary satellite Himawari-8. This satellite provides ten minute imagery in sixteen wavebands over the Asian and Australasian region. The imagery has been navigated, calibrated and subsequently used in the Bureau of Meteorology (BoM) to generate Atmospheric Motion Vectors (AMVs) over the full earth disk viewed from the satellite every ten minutes. Each vector has been error characterised and assigned an expected error. In preparation for the operational assimilation of the ten minute data, these high temporal and spatial resolution data were used with the BoM operational database to provide forecasts from the next generation operational forecast model ACCESS APS2 using 4D Var. Results from these tests indicate these locally generated Himawari-8 ten minute AMVs are of high density and quality and have the potential to improve numerical weather prediction (NWP) model initialisation and forecasts. The forecasts undertaken include cases associated with extreme weather. The results also provided the appropriate times, data selection and application methods for the effective use of these high temporal resolution data. As a result of these studies these wind data were approved for inclusion in the BoMs operational database and are used in operational forecasting.

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.

scholarly journals Validation of Satellite-Derived Atmospheric Motion Vectors and Analyses around Tropical Disturbances

2012 ◽  
Vol 51 (10) ◽  
pp. 1823-1834 ◽  
Author(s):  
John Sears ◽  
Christopher S. Velden
Keyword(s):  
Weather Prediction ◽  
Flow Fields ◽  
Observation Error ◽  
Motion Vectors ◽  
Tropical Oceans ◽  
Atmospheric Motion ◽  
The Tropics ◽  
Atmospheric Motion Vectors ◽  
Dependent Observation

AbstractFields of atmospheric motion vectors (AMVs) are routinely derived by tracking features in sequential geostationary satellite infrared, water vapor, and visible-channel imagery. While AMVs produced operationally by global data centers are routinely evaluated against rawinsondes, there is a relative dearth of validation opportunities over the tropical oceans—in particular, in the vicinity of tropical disturbances when anomalous flow fields and strongly sheared environments commonly exist. A field experiment in 2010 called Pre-Depression Investigation of Cloud-Systems in the Tropics (PREDICT) was conducted in the tropical west Atlantic Ocean and provides an opportunity to evaluate the quality of tropical AMVs and analyses derived from them. The importance of such a verification is threefold: 1) AMVs often provide the only input data for numerical weather prediction (NWP) over cloudy areas of the tropical oceans, 2) NWP data assimilation methods are increasingly reliant on accurate flow-dependent observation-error characteristics, and 3) global tropical analysis and forecast centers often rely on analyses and diagnostic products derived from the AMV fields. In this paper, the authors utilize dropsonde information from high-flying PREDICT aircraft to identify AMV characteristics and to better understand their errors in tropical-disturbance situations. It is found that, in general, the AMV observation errors are close to those identified in global validation studies. However, some distinct characteristics are uncovered in certain regimes associated with tropical disturbances. High-resolution analyses derived from the AMV fields are also examined and are found to be more reflective of anomalous flow fields than the respective Global Forecast System global model analyses.

Consistent Robust and Recursive Estimation of Atmospheric Motion Vectors From Satellite Images

2019 ◽  
Vol 57 (3) ◽  
pp. 1538-1544
Author(s):  
Kalamraju Mounika ◽  
Sheeba Rani J ◽  
Govindan Kutty ◽  
Sai Subrahmanyam R. K. Gorthi
Keyword(s):  
Satellite Images ◽  
Recursive Estimation ◽  
Motion Vectors ◽  
Atmospheric Motion ◽  
Atmospheric Motion Vectors

scholarly journals First results from the generation and assimilation of 10-minute atmospheric motion vectors in the Australian region

2016 ◽  
Vol 66 (1) ◽  
pp. 12-18
Author(s):  
Yi Xiao ◽  
David Howard ◽  
Chris Tingwell ◽  
Jeff Freeman ◽  
Paul Gregory ◽  
...  
Keyword(s):  
Motion Vectors ◽  
Australian Region ◽  
First Results ◽  
Atmospheric Motion ◽  
Atmospheric Motion Vectors

scholarly journals Strategies for Assimilating High-Density Atmospheric Motion Vectors into a Regional Tropical Cyclone Forecast Model (HWRF)

Atmosphere ◽  
2020 ◽  
Vol 11 (6) ◽  
pp. 673 ◽  
Author(s):  
William E. Lewis ◽  
Christopher S. Velden ◽  
David Stettner
Keyword(s):  
Forecast Error ◽  
Forecast Model ◽  
High Density ◽  
Track Forecast ◽  
Forecast Errors ◽  
Spatiotemporal Resolution ◽  
Motion Vectors ◽  
Weather Events ◽  
Atmospheric Motion ◽  
Atmospheric Motion Vectors

In recent years, atmospheric numerical modeling frameworks and satellite observing systems have both undergone significant advances. While these developments offer considerable potential for improving forecasts of high-impact weather events such as tropical cyclones (TC), much work remains to be done regarding the targeted processing and optimal use of observations now becoming available with high spatiotemporal resolution. Using the 2019 version of NCEP’s HWRF model, we explore several different strategies for the assimilation of TC-scale, high-density atmospheric motion vectors (AMVs) derived from the new-generation GOES-R series of geostationary satellites. Using 2017’s Atlantic Hurricane Irma as a case study, we examine the HWRF forecast impacts of observation pre-processing, including thinning and adjustments to observation errors. It is demonstrated that enhanced vortex-scale GOES-16 AMVs contribute to notable improvements in HWRF track forecast error compared to a baseline control experiment that does not incorporate the high-density AMVs. Impacts on TC intensity and structure (i.e., wind radii) forecast errors are less robust, but results from the optimization experiments suggest that further work (both with regard to data assimilation strategies and advancements in the methods themselves) should lead to improvements in these forecast variables as well.

scholarly journals Atmospheric Motion Vectors from Model Simulations. Part I: Methods and Characterization as Single-Level Estimates of Wind

2014 ◽  
Vol 53 (1) ◽  
pp. 47-64 ◽  
Author(s):  
Niels Bormann ◽  
Angeles Hernandez-Carrascal ◽  
Régis Borde ◽  
Hans-Joachim Lutz ◽  
Jason A. Otkin ◽  
...  
Keyword(s):  
Weather Prediction ◽  
Real Data ◽  
Forecast Model ◽  
Random Errors ◽  
Motion Vectors ◽  
Spatial Error ◽  
Single Level ◽  
Vector Difference ◽  
Atmospheric Motion ◽  
Atmospheric Motion Vectors

AbstractThe objective of this study is to improve the characterization of satellite-derived atmospheric motion vectors (AMVs) and their errors to guide developments in the use of AMVs in numerical weather prediction. AMVs tend to exhibit considerable systematic and random errors that arise in the derivation or the interpretation of AMVs as single-level point observations of wind. One difficulty in the study of AMV errors is the scarcity of collocated observations of clouds and wind. This study uses instead a simulation framework: geostationary imagery for Meteosat-8 is generated from a high-resolution simulation with the Weather Research and Forecasting regional model, and AMVs are derived from sequences of these images. The forecast model provides the “truth” with a sophisticated description of the atmosphere. The study considers infrared and water vapor AMVs from cloudy scenes. This is the first part of a two-part paper, and it introduces the framework and provides a first evaluation in terms of the brightness temperatures of the simulated images and the derived AMVs. The simulated AMVs show a considerable global bias in the height assignment (60–75 hPa) that is not observed in real AMVs. After removal of this bias, however, the statistics comparing the simulated AMVs with the true model wind show characteristics that are similar to statistics comparing real AMVs with short-range forecasts (speed bias and root-mean-square vector difference typically agree to within 1 m s−1). This result suggests that the error in the simulated AMVs is comparable to or larger than that in real AMVs. There is evidence for significant spatial, temporal, and vertical error correlations, with the scales for the spatial error correlations being consistent with estimates for real data.

scholarly journals Spatiotemporal Fusion of Formosat-2 and Landsat-8 Satellite Images: A Comparison of “Super Resolution-Then-Blend” and “Blend-Then-Super Resolution” Approaches

2021 ◽  
Vol 13 (4) ◽  
pp. 606
Author(s):  
Tee-Ann Teo ◽  
Yu-Ju Fu
Keyword(s):  
Satellite Images ◽  
Traditional Approach ◽  
Spectral Band ◽  
Super Resolution ◽  
High Temporal Resolution ◽  
Landsat 8 ◽  
Fusion Model ◽  
Image Blending ◽  
Fine Resolution

The spatiotemporal fusion technique has the advantages of generating time-series images with high-spatial and high-temporal resolution from coarse-resolution to fine-resolution images. A hybrid fusion method that integrates image blending (i.e., spatial and temporal adaptive reflectance fusion model, STARFM) and super-resolution (i.e., very deep super resolution, VDSR) techniques for the spatiotemporal fusion of 8 m Formosat-2 and 30 m Landsat-8 satellite images is proposed. Two different fusion approaches, namely Blend-then-Super-Resolution and Super-Resolution (SR)-then-Blend, were developed to improve the results of spatiotemporal fusion. The SR-then-Blend approach performs SR before image blending. The SR refines the image resampling stage on generating the same pixel-size of coarse- and fine-resolution images. The Blend-then-SR approach is aimed at refining the spatial details after image blending. Several quality indices were used to analyze the quality of the different fusion approaches. Experimental results showed that the performance of the hybrid method is slightly better than the traditional approach. Images obtained using SR-then-Blend are more similar to the real observed images compared with images acquired using Blend-then-SR. The overall mean bias of SR-then-Blend was 4% lower than Blend-then-SR, and nearly 3% improvement for overall standard deviation in SR-B. The VDSR technique reduces the systematic deviation in spectral band between Formosat-2 and Landsat-8 satellite images. The integration of STARFM and the VDSR model is useful for improving the quality of spatiotemporal fusion.

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.

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