Characteristics of Fengyun-4A Satellite Atmospheric Motion Vectors and Their Impacts on Data Assimilation

2020 ◽  
Vol 37 (11) ◽  
pp. 1222-1238
Author(s):  
Yaodeng Chen ◽  
Jie Shen ◽  
Shuiyong Fan ◽  
Deming Meng ◽  
Cheng Wang
Keyword(s):  
Data Assimilation ◽  
Motion Vectors ◽  
Atmospheric Motion ◽  
Atmospheric Motion Vectors

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.

scholarly journals Impact of Enhanced Atmospheric Motion Vectors on HWRF Hurricane Analyses and Forecasts with Different Data Assimilation Configurations

2018 ◽  
Vol 146 (5) ◽  
pp. 1549-1569 ◽  
Author(s):  
Shixuan Zhang ◽  
Zhaoxia Pu ◽  
Christopher Velden
Keyword(s):  
Data Assimilation ◽  
Inner Core ◽  
Core Region ◽  
Intensity Change ◽  
Motion Vectors ◽  
Processing Strategies ◽  
Negative Effect ◽  
Atmospheric Motion ◽  
Environmental Prediction ◽  
Atmospheric Motion Vectors

Abstract The impacts of enhanced satellite-derived atmospheric motion vectors (AMVs) on the numerical prediction of intensity changes during Hurricanes Gonzalo (2014) and Joaquin (2015) are examined. Enhanced AMVs benefit from special data-processing strategies and are examined for impact on model forecasts via assimilation experiments by employing the National Centers for Environmental Prediction (NCEP) operational Hurricane Weather Research and Forecasting (HWRF) Model using a Gridpoint Statistical Interpolation analysis system (GSI)-based ensemble–variational hybrid system. Two different data assimilation (DA) configurations, one with and one without the use of vortex initialization (VI), are compared. It is found that the assimilation of enhanced AMVs can improve the HWRF track and intensity forecasts of Gonzalo and Joaquin during their intensity change phases. The degree of data impact depends on the DA configuration used. Overall, assimilation of enhanced AMVs in the innermost domain (e.g., storm inner-core region and its immediate vicinity) outperforms other DA configurations, both with and without VI, as it results in better track and intensity forecasts. Compared to the experiment with VI, assimilation of enhanced AMVs without VI reveals more notable data impact on the forecasts of Hurricane Gonzalo, as the VI before DA alters the first guess and reduces the actual number of AMV observations assimilated into the DA system. Even with VI, assimilation of enhanced AMVs in the inner-core region can at least partially mitigate the negative effect of VI on the intensity forecast of Hurricane Gonzalo and alleviate the unrealistic vortex weakening in the simulation by removing unrealistic outflow structure and unfavorable thermodynamic conditions, thus leading to improved intensity forecasts.

scholarly journals Impact of local data assimilation on tropical cyclone predictions over the Bay of Bengal using the ARW model

2015 ◽  
Vol 33 (7) ◽  
pp. 805-828 ◽  
Author(s):  
M. M. Greeshma ◽  
C. V. Srinivas ◽  
V. Yesubabu ◽  
C. V. Naidu ◽  
R. Baskaran ◽  
...  
Keyword(s):  
Tropical Cyclone ◽  
Data Assimilation ◽  
Bay Of Bengal ◽  
Weather Research And Forecasting ◽  
Motion Vectors ◽  
Arw Model ◽  
Atmospheric Motion ◽  
Atmospheric Motion Vectors ◽  
Radiance Data ◽  
Weather Research

Abstract. The tropical cyclone (TC) track and intensity predictions over Bay of Bengal (BOB) using the Advanced Research Weather Research and Forecasting (ARW) model are evaluated for a number of data assimilation experiments using various types of data. Eight cyclones that made landfall along the east coast of India during 2008–2013 were simulated. Numerical experiments included a control run (CTL) using the National Centers for Environmental Prediction (NCEP) 3-hourly 0.5 × 0.5° resolution Global Forecasting System (GFS) analysis as the initial condition, and a series of cycling mode variational assimilation experiments with Weather Research and Forecasting (WRF) data assimilation (WRFDA) system using NCEP global PrepBUFR observations (VARPREP), Atmospheric Motion Vectors (VARAMV), Advanced Microwave Sounding Unit (AMSU) A and B radiances (VARRAD) and a combination of PrepBUFR and RAD (VARPREP+RAD). The impact of different observations is investigated in detail in a case of the strongest TC, Phailin, for intensity, track and structure parameters, and finally also on a larger set of cyclones. The results show that the assimilation of AMSU radiances and Atmospheric Motion Vectors (AMV) improved the intensity and track predictions to a certain extent and the use of operationally available NCEP PrepBUFR data which contains both conventional and satellite observations produced larger impacts leading to improvements in track and intensity forecasts. The forecast improvements are found to be associated with changes in pressure, wind, temperature and humidity distributions in the initial conditions after data assimilation. The assimilation of mass (radiance) and wind (AMV) data showed different impacts. While the motion vectors mainly influenced the track predictions, the radiance data merely influenced forecast intensity. Of various experiments, the VARPREP produced the largest impact with mean errors (India Meteorological Department (IMD) observations less the model values) of 78, 129, 166, 210 km in the vector track position, 10.3, 5.8, 4.8, 9.0 hPa deeper than IMD data in central sea level pressure (CSLP) and 10.8, 3.9, −0.2, 2.3 m s−1 stronger than IMD data in maximum surface winds (MSW) for 24, 48, 72, 96 h forecasts respectively. An improvement of about 3–36 % in track, 6–63 % in CSLP, 26–103 % in MSW and 11–223 % in the radius of maximum winds in 24–96 h lead time forecasts are found with VARPREP over CTL, suggesting the advantages of assimilation of operationally available PrepBUFR data for cyclone predictions. The better predictions with PrepBUFR could be due to quality-controlled observations in addition to containing different types of data (conventional, satellite) covering an effectively larger area. The performance degradation of VARPREP+RAD with the assimilation of all available observations over the domain after 72 h could be due to poor area coverage and bias in the radiance data.

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.

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