scholarly journals Assimilation Experiments of MTSAT Rapid Scan Atmospheric Motion Vectors on a Heavy Rainfall Event

2015 ◽  
Vol 93 (4) ◽  
pp. 459-475 ◽  
Author(s):  
Michiko OTSUKA ◽  
Masaru KUNII ◽  
Hiromu SEKO ◽  
Kazuki SHIMOJI ◽  
Masahiro HAYASHI ◽  
...  
Keyword(s):  
Heavy Rainfall ◽  
Rainfall Event ◽  
Heavy Rainfall Event ◽  
Motion Vectors ◽  
Rapid Scan ◽  
Atmospheric Motion ◽  
Atmospheric Motion Vectors

scholarly journals Assimilation of Himawari-8 Rapid-Scan Atmospheric Motion Vectors on Tropical Cyclone in HWRF System

Atmosphere ◽  
2020 ◽  
Vol 11 (6) ◽  
pp. 601
Author(s):  
Masahiro Sawada ◽  
Zaizhong Ma ◽  
Avichal Mehra ◽  
Vijay Tallapragada ◽  
Ryo Oyama ◽  
...  
Keyword(s):  
Tropical Cyclone ◽  
Lead Time ◽  
Forecast Skill ◽  
Track Forecast ◽  
Motion Vectors ◽  
Substantial Impact ◽  
Rapid Scan ◽  
Forecast Lead Time ◽  
Atmospheric Motion ◽  
Atmospheric Motion Vectors

This study investigates the assimilation impact of rapid-scan (RS) atmospheric motion vectors (AMVs) derived from the geostationary satellite Himawari-8 on tropical cyclone (TC) forecasts. Forecast experiments for three TCs in 2016 in the western North Pacific basin are performed using the National Centers for Environmental Prediction (NCEP) operational Hurricane Weather Research and Forecasting Model (HWRF). An ensemble-variational hybrid data assimilation system is used as an initialization. The results show that the assimilation of RS-AMVs can improve the track forecast skill, while the weak bias or slow intensification bias increases at the shorter forecast lead time. A vortex initialization in HWRF has a substantial impact on TC structure, but it has neutral impacts on the track and intensity forecasts. A thinning of AMVs mitigates the weak bias caused by RS-AMV assimilation, resulting in the reduction of intensity error. However, it degrades the track forecast skill for a longer lead time. A decomposition of the TC steering flows demonstrated that the change in TC-induced flow was a primary factor for reducing the track forecast error, and the change in environmental flow has less impact on the track forecast. The investigation of the structural change from the assimilation of RS-AMV revealed that the following two factors are likely related to the intensity forecast degradation: (1) an increase of inertial stability outside the radius of maximum wind (RMW), which weakens the boundary layer inflow; and (2) a drying around and outside the RMW.

Retrieval and Application of Rapid-Scan Atmospheric Motion Vectors Using an Infrared Channel of the INSAT-3DR Satellite

2021 ◽  
Author(s):  
Dineshkumar K. Sankhala ◽  
Prashant Kumar ◽  
Sanjib K. Deb ◽  
Neeru Jaiswal ◽  
C. M. Kishtawal ◽  
...  
Keyword(s):  
Motion Vectors ◽  
Rapid Scan ◽  
Atmospheric Motion ◽  
Atmospheric Motion Vectors ◽  
Infrared Channel

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 Role of land state in a high resolution mesoscale model for simulating the Uttarakhand heavy rainfall event over India

2016 ◽  
Vol 125 (3) ◽  
pp. 475-498 ◽  
Author(s):  
P V Rajesh ◽  
S Pattnaik ◽  
D Rai ◽  
K K Osuri ◽  
U C Mohanty ◽  
...  
Keyword(s):  
High Resolution ◽  
Heavy Rainfall ◽  
Mesoscale Model ◽  
Rainfall Event ◽  
Heavy Rainfall Event
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