Influence of Assimilating Satellite-Derived Atmospheric Motion Vector Observations on Numerical Analyses and Forecasts of Tropical Cyclone Track and Intensity

2014 ◽  
Vol 142 (1) ◽  
pp. 49-71 ◽  
Author(s):  
Ting-Chi Wu ◽  
Hui Liu ◽  
Sharanya J. Majumdar ◽  
Christopher S. Velden ◽  
Jeffrey L. Anderson
Keyword(s):  
Motion Vector ◽  
Optimal Strategies ◽  
Weather Research And Forecasting ◽  
Ensemble Forecasts ◽  
University Of Wisconsin ◽  
Full Resolution ◽  
Rapid Scan ◽  
Order Of Magnitude ◽  
Atmospheric Motion ◽  
Atmospheric Motion Vectors

Abstract The influence of assimilating enhanced atmospheric motion vectors (AMVs) on mesoscale analyses and forecasts of tropical cyclones (TC) is investigated. AMVs from the geostationary Multifunctional Transport Satellite (MTSAT) are processed by the Cooperative Institute for Meteorological Satellite Studies (CIMSS, University of Wisconsin–Madison) for the duration of Typhoon Sinlaku (2008), which included a rapid intensification phase and a slow, meandering track. The ensemble Kalman filter and the Weather Research and Forecasting Model are utilized within the Data Assimilation Research Testbed. In addition to conventional observations, three different groups of AMVs are assimilated in parallel experiments: CTL, the same dataset assimilated in the NCEP operational analysis; CIMSS(h), hourly datasets processed by CIMSS; and CIMSS(h+RS), the dataset including AMVs from the rapid-scan mode. With an order of magnitude more AMV data assimilated, the CIMSS(h) analyses exhibit a superior track, intensity, and structure to CTL analyses. The corresponding 3-day ensemble forecasts initialized with CIMSS(h) yield smaller track and intensity errors than those initialized with CTL. During the period when rapid-scan AMVs are available, the CIMSS(h+RS) analyses offer additional modifications to the TC and its environment. In contrast to many members in the ensemble forecasts initialized from the CTL and CIMSS(h) analyses that predict an erroneous landfall in China, the CIMSS(h+RS) members capture recurvature, albeit prematurely. The results demonstrate the promise of assimilating enhanced AMV data into regional TC models. Further studies to identify optimal strategies for assimilating integrated full-resolution multivariate data from satellites are under way.

scholarly journals Forecast Sensitivity Observation Impact in the 4DVAR and Hybrid-4DVAR Data Assimilation Systems

2019 ◽  
Vol 36 (8) ◽  
pp. 1563-1575 ◽  
Author(s):  
Sung-Min Kim ◽  
Hyun Mee Kim
Keyword(s):  
Data Assimilation ◽  
East Asia ◽  
Forecast Error ◽  
Integration Time ◽  
Ensemble Forecasts ◽  
Background Error ◽  
Error Covariance ◽  
Atmospheric Motion ◽  
Observation Impact ◽  
Atmospheric Motion Vectors

AbstractIn this study, the observation impacts on 24-h forecast error reduction (FER), based on the adjoint method in the four-dimensional variational (4DVAR) data assimilation (DA) and hybrid-4DVAR DA systems coupled with the Unified Model, were evaluated from 0000 UTC 5 August to 1800 UTC 26 August 2014. The nonlinear FER in hybrid-4DVAR was 12.2% greater than that in 4DVAR due to the use of flow-dependent background error covariance (BEC), which was a weighted combination of the static BEC and the ensemble BEC based on ensemble forecasts. In hybrid-4DVAR, the observation impacts (i.e., the approximated nonlinear FER) for most observation types increase compared to those in 4DVAR. The increased observation impact from using hybrid-4DVAR instead of 4DVAR changes depending on the analysis time and regions. To calculate the ensemble BEC in hybrid-4DVAR, analyses at 0600 and 1800 UTC (0000 and 1200 UTC) used 3-h (9-h) ensemble forecasts. Greater observation impact was obtained when 3-h ensemble forecasts were used for the ensemble BEC at 0600 and 1800 UTC, than with 9-h ensemble forecasts at 0000 and 1200 UTC. Different from other observations, the atmospheric motion vectors (AMVs) deduced from geostationary satellite are more frequently observed in the same area. When the ensemble forecasts with longer integration times were used for the ensemble BEC in hybrid-4DVAR, the observation impact of the AMVs decreased the most in East Asia. This implies that the observation impact of AMVs in East Asia shows the highest sensitivity to the integration time of the ensemble members used for deducing the flow-dependent BEC in hybrid-4DVAR.

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.

scholarly journals Assessment of MISR Cloud Motion Vectors (CMVs) Relative to GOES and MODIS Atmospheric Motion Vectors (AMVs)

2017 ◽  
Vol 56 (3) ◽  
pp. 555-572 ◽  
Author(s):  
Kevin J. Mueller ◽  
Dong L. Wu ◽  
Ákos Horváth ◽  
Veljko M. Jovanovic ◽  
Jan-Peter Muller ◽  
...  
Keyword(s):  
Standard Deviation ◽  
Motion Vector ◽  
Motion Vectors ◽  
Cloud Motion ◽  
Modis Imagery ◽  
Moderate Resolution ◽  
Atmospheric Motion ◽  
Moderate Resolution Imaging Spectroradiometer ◽  
Atmospheric Motion Vectors ◽  
Along Track

AbstractCloud motion vector (CMV) winds retrieved from the Multiangle Imaging SpectroRadiometer (MISR) instrument on the polar-orbiting Terra satellite from 2003 to 2008 are compared with collocated atmospheric motion vectors (AMVs) retrieved from Geostationary Operational Environmental Satellite (GOES) imagery over the tropics and midlatitudes and from Moderate Resolution Imaging Spectroradiometer (MODIS) imagery near the poles. MISR imagery from multiple view angles is exploited to jointly retrieve stereoscopic cloud heights and motions, showing advantages over the AMV heights assigned by radiometric means, particularly at low heights (<3 km) that account for over 95% of MISR CMV sampling. MISR–GOES wind differences exhibit a standard deviation ranging with increasing height from 3.3 to 4.5 m s−1 for a high-quality [quality indicator (QI) ≥ 80] subset where height differences are <1.5 km. Much of the observed difference can be attributed to the less accurately retrieved component of CMV motion along the direction of satellite motion. MISR CMV retrieval is subject to correlation between error in retrieval of this along-track component and of height. This manifests as along-track bias varying with height to magnitudes as large as 2.5 m s−1. The cross-track component of MISR CMVs shows small (<0.5 m s−1) bias and standard deviation of differences (1.7 m s−1) relative to GOES AMVs. Larger differences relative to MODIS are attributed to the tracking of cloud features at heights lower than MODIS in multilayer cloud scenes.

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.

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 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.

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 Impact of the Tracer Size and the Temporal Gap between Images in the Extraction of Atmospheric Motion Vectors

2014 ◽  
Vol 31 (8) ◽  
pp. 1761-1770 ◽  
Author(s):  
Javier García-Pereda ◽  
Régis Borde
Keyword(s):  
Motion Vector ◽  
Mediterranean Area ◽  
Small Impact ◽  
Vector Difference ◽  
Atmospheric Motion ◽  
The Mean ◽  
Atmospheric Motion Vectors ◽  
Ecmwf Model ◽  
The Impact ◽  
Pixel Scale

Abstract The goal of this paper is to show the impact of the tracer size and the temporal gap between images in atmospheric motion vector (AMV) extraction schemes. A test has been performed using NWC SAF/High Resolutions Winds AMV software for different configurations with a tracer size varying between 8 × 8 and 40 × 40 pixels and a temporal gap between images varying between 5 and 90 min. AMVs have been extracted for four different MSG/SEVIRI channels (HRVIS, VIS0.8, WV6.2, and IR10.8) over the European and Mediterranean area for a 6-month period (January–June 2010). The AMV performances have been tested against radiosonde winds and ECMWF model analysis winds. The results show a small impact of the tracer size on the number of valid AMVs, which is, however, more significant for clear air AMVs, and a significant impact of the temporal gap between images. The largest number of valid AMVs has been found in general for a temporal gap of 5 min for the 1-km pixel scale and for a temporal gap of 10 min for the 3-km pixel scale. Results also show a decrease of the mean AMV speed and the normalized BIAS (NBIAS) with larger tracer sizes, and a relatively small impact of the temporal gap on these parameters. Finally, the results show minimum values of the normalized root-mean-square vector difference (NRMSVD) for intermediate temporal gaps between 15 and 30 min with a relatively small impact of the tracer size on this parameter.

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.

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