Atmospheric Motion Vectors Research Articles

Evolution of the upper tropospheric outflow in Hurricanes Iselle and Julio (2014) in the Navy Global Environmental Model (NAVGEM) analyses and in satellite and dropsonde observations

AbstractUpper tropospheric outflow was examined during the life cycles of two hurricanes in the eastern and central Pacific Ocean. The outflow from Hurricanes Iselle and Julio was evaluated by using analyses from the Navy Global Environmental Model, which were very highly correlated with satellite atmospheric motion vector and NOAA G‐IV dropsonde observations. A synoptic overview provided the environmental context for the life cycles of both tropical cyclones (TCs). Then, the outflow magnitude and direction within 6 radial degrees of each TC center were analyzed in relation to TC intensity, the synoptic environment, and inertial stability, with the following results. In both Iselle and Julio, the azimuthally averaged outflow magnitude was maximized initially more than 4 radial degrees from the center, and that maximum moved steadily inward during a 4 day intensification period and reached a position radially inward of 2° within 6 h of the time of maximum surface winds. Furthermore, the direction of the outflow in both TCs was related to the evolution of the large‐scale upper tropospheric flow pattern, particularly the phasing of subtropical jet ridges and troughs moving eastward north of both TCs. Finally, outflow channels were consistently bounded by regions of lowest (highest) values of inertial stability counterclockwise (clockwise) from the maximum outflow azimuth, a pattern that persisted throughout the life cycles of both storms regardless of intensity, environmental flow, and the number and direction of outflow channels present.

Lidar-Based Height Correction for the Assimilation of 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.

Analysis of Mesoscale Atmospheric Flows above Mature Deep Convection Using Super Rapid Scan Geostationary Satellite Data

AbstractSuper Rapid Scan Operations for the Geostationary Operational Environmental Satellite (GOES) R series (SRSOR) using GOES-14 have made experimentation with 1-min time-step data possible prior to the launch of the new satellite. A mesoscale atmospheric motion vector (mAMV) program is utilized in SRSOR with a Barnes analysis to produce objectively analyzed flow fields at the cloud tops of deep convection. Two nonsupercell and four supercell storm cases are analyzed. Data from the SRSOR mAMV analysis are compared with both multi-Doppler analyses when available and idealized convection cases within the Weather Research and Forecasting (WRF) Model framework. It is found that using SRSOR data provides several additional trackable targets to produce mAMVs in rapidly “bubbling” regions at the deep convective cloud-top level not previously available at lower temporal resolutions (<1 min). Results also show that supercell storm cases produce long-lived maxima in SRSOR cloud-top divergence (CTD) and “couplet” signatures in cloud-top vorticity (CTV), which when compared with idealized WRF Model simulations appear to form as a result of environmental horizontal vorticity tilting. Nonsupercell convection in contrast produced weaker, short-lived CTD signatures and no “CTV couplet” signatures. These case study results suggest that with SRSOR data it might be possible to uniquely identify supercells using only mAMV-derived flow fields.

Deriving AMVs from Geostationary Satellite Images Using Optical Flow Algorithm Based on Polynomial Expansion

AbstractAn optical flow algorithm based on polynomial expansion (OFAPE) was used to derive atmospheric motion vectors (AMVs) from geostationary satellite images. In OFAPE, there are two parameters that can affect the AMV results: the sizes of the expansion window and optimization window. They should be determined according to the temporal interval and spatial resolution of satellite images. A helpful experiment was conducted for selecting those sizes. The limitations of window sizes can cause loss of strong wind speed, and an image-pyramid scheme was used to overcome this problem. Determining the heights of AMVs for semitransparent cloud pixels (STCPs) is challenging work in AMV derivation. In this study, two-dimensional histograms (H2Ds) between infrared brightness temperatures (6.7- and 10.8-μm channels) formed from a long time series of cloud images were used to identify the STCPs and to estimate their actual temperatures/heights. The results obtained from H2Ds were contrasted with CloudSat radar reflectivity and CALIPSO cloud-feature mask data. Finally, in order to verify the algorithm adaptability, three-month AMVs (JJA 2013) were calculated and compared with the wind fields of ERA data and the NOAA/ESRL radiosonde observations in three aspects: speed, direction, and vector difference.

Solar Power Prediction Based on Satellite Images and Support Vector Machine

Penetration of solar energy into main grid has gradually increased in recent years due to a growing number of large-scale photovoltaic (PV) farms. The power output of these PV farms may fluctuate due to a wide variability of meteorological conditions, and, thus, we need to compensate for this effect in advance. In this paper, we propose a solar power prediction model based on various satellite images and a support vector machine (SVM) learning scheme. The motion vectors of clouds are forecasted by utilizing satellite images of atmospheric motion vectors (AMVs). We analyze 4 years’ historical satellite images and utilize them to configure a large number of input and output data sets for the SVM learning. We compare the performance of the proposed SVM-based model, the conventional time-series model, and an artificial neural network (ANN) model in terms of prediction accuracy.

Using satellite-derived Atmospheric Motion Vector (AMV) observations in the ensemble data assimilation system

The use of global Atmospheric Motion Vectors (AMV) satellite observations in the meteorological data assimilation system based on Local Ensemble Transform Kalman Filter (LETKF) algorithm is considered. The height assignment is the most crucial error source for AMV observations. To reduce its impact, the AMV height reassignment method is implemented; it is based on the consistency coefficient bet ween the observed and the background winds. The other way to improve the analysis quality is a more accurate specification of AMV observation errors. This necessitates the use of the nondiagonal observation-error covariance matrix R in the data assimilation scheme. The first results of these studies are presented. It is demonstrated that the use of AMV observations in the data assimilation system reduces the errors of forecasts computed from the initial data of this system.

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

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. In late 2013 and early 2014 the Japanese Meteorological Agency (JMA) provided several months of MTSat-1R rapid scan imagery in support of the 2014 High Altitude Ice Crystal (HAIC) – High Ice Water Content (HIWC) field campaign in the Darwin area. This 10-minute imagery was also provided in support of activities in preparation for Himawari-8. The imagery was renavigated, calibrated and used to generate atmospheric motion vectors (AMVs) over much of the Australian region including the HAIC-HIWC test area. Each vector was error characterised and assigned an expected error. In preparation for the operational assimilation of 10-minute data from the full disk footprint of Himawari-8, these high temporal and spatial resolution data generated over the Australian region test area were combined with the operational database—including high spatial but lower temporal density local Bureau and also JMA AMV data from MTSat-2—to provide forecasts using the next generation operational regional forecast model ACCESS APS2. This model will be used during the introduction of operational Himawari-8 data. First results from these tests indicate the 10-minute data have the potential to produce high density, high quality AMVs which will improve model initialisation and forecasts. This includes cases associated with extreme weather. The results have also provided indications of appropriate timing, data selection and application methods for the effective use of these high temporal resolution data. The quality and density of the 10-minute MTSat-1R data have also been compared to that available from imagery using a new processing paradigm which is to be used in future for the operational Himawari-8 AMV generation.

EUMETSAT Global AVHRR Wind Product

AbstractEUMETSAT has been deriving atmospheric motion vectors (AMV) operationally from the EUMETSAT Polar System satellite MetOp over polar regions since 2011. The launch of MetOp-B in 2012 permitted doubling the frequency of extracting AMVs using AVHRR imagery data of both MetOp-A and -B satellites. The tandem configuration with two satellites on the same orbital plane, but with a phase difference, provided an interesting opportunity to create global AMVs from the satellites with a significant overlap in imagery data. EUMETSAT has therefore developed a new global AVHRR winds product derived from a pair of MetOp-A and MetOp-B images that has been operational since January 2015. The temporal gap between the two images used for tracking clouds is about 50 min. The global coverage of this new wind product allows for a homogeneous retrieval of wind product over the whole globe, including the polar regions. This clearly helps fill the gaps between 55° and 70° latitude north and south, where only few wind observations are currently available for assimilation into numerical weather prediction models. The new global AVHRR wind product can be directly compared with AMVs derived from geostationary satellites. This paper describes the scientific concept of wind extraction using dual MetOp satellites. It highlights the performance of the new global AVHRR wind product by comparing with collocated AMVs extracted from the EUMETSAT geostationary satellites Meteosat-7 and Meteosat-10.

Impact of assimilation of INSAT-3D retrieved atmospheric motion vectors on short-range forecast of summer monsoon 2014 over the South Asian region

The Weather Research and Forecasting (WRF) model and its three-dimensional variational data assimilation system are used in this study to assimilate the INSAT-3D, a recently launched Indian geostationary meteorological satellite derived from atmospheric motion vectors (AMVs) over the South Asian region during peak Indian summer monsoon month (i.e., July 2014). A total of four experiments were performed daily with and without assimilation of INSAT-3D-derived AMVs and the other AMVs available through Global Telecommunication System (GTS) for the entire month of July 2014. Before assimilating these newly derived INSAT-3D AMVs in the numerical model, a preliminary evaluation of these AMVs is performed with National Centers for Environmental Prediction (NCEP) final model analyses. The preliminary validation results show that root-mean-square vector difference (RMSVD) for INSAT-3D AMVs is ∼3.95, 6.66, and 5.65 ms−1 at low, mid, and high levels, respectively, and slightly more RMSVDs are noticed in GTS AMVs (∼4.0, 8.01, and 6.43 ms−1 at low, mid, and high levels, respectively). The assimilation of AMVs has improved the WRF model of produced wind speed, temperature, and moisture analyses as well as subsequent model forecasts over the Indian Ocean, Arabian Sea, Australia, and South Africa. Slightly more improvements are noticed in the experiment where only the INSAT-3D AMVs are assimilated compared to the experiment where only GTS AMVs are assimilated. The results also show improvement in rainfall predictions over the Indian region after AMV assimilation. Overall, the assimilation of INSAT-3D AMVs improved the WRF model short-range predictions over the South Asian region as compared to control experiments.

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

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.

Ensemble Data Assimilation and Forecast Experiments for the September 2015 Heavy Rainfall Event in Kanto and Tohoku Regions with Atmospheric Motion Vectors from Himawari-8

Ensemble Data Assimilation and Forecast Experiments for the September 2015 Heavy Rainfall Event in Kanto and Tohoku Regions with Atmospheric Motion Vectors from Himawari-8

Intensification of Typhoon Danas (1324) Captured by MTSAT Upper Tropospheric Atmospheric Motion Vectors

Intensification of Typhoon Danas (1324) Captured by MTSAT Upper Tropospheric Atmospheric Motion Vectors

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

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

가우시안 군집분석을 이용한 천리안 위성의 대기운동벡터 표적추적 알고리듬 개발 및 분석

Atmospheric Motion Vector (AMV) from satellite images have shown Slow Speed Bias (SSB) in comparison with rawinsonde. The causes of SSB are originated from tracking, selection, and height assignment error, which is known to be the leading error. However, recent works have shown that height assignment error cannot be fully explained the cause of SSB. This paper attempts a new approach to examine the possibility of SSB reduction of COMS AMV by using a new target tracking algorithm. Tracking error can be caused by averaging of various wind patterns within a target and changing of cloud shape in searching process over time. To overcome this problem, Gaussian Mixture Model (GMM) has been adopted to extract the coldest cluster as target since the shape of such target is less subject to transformation. Then, an image filtering scheme is applied to weigh more on the selected coldest pixels than the other, which makes it easy to track the target. When AMV derived from our algorithm with sum of squared distance method and current COMS are compared with rawindsonde, our products show noticeable improvement over COMS products in mean wind speed by an increase of and SSB reduction by 29%. However, the statistics regarding the bias show negative impact for mid/low level with our algorithm, and the number of vectors are reduced by 40% relative to COMS. Therefore, further study is required to improve accuracy for mid/low level winds and increase the number of AMV vectors.

Assimilation of wind speed and direction observations: results from real observation experiments

The assimilation of wind observations in the form of speed and direction (asm_sd) by the Weather Research and Forecasting Model Data Assimilation System (WRFDA) was performed using real data and employing a series of cycling assimilation experiments for a 2-week period, as a follow-up for an idealised post hoc assimilation experiment. The satellite-derived Atmospheric Motion Vectors (AMV) and surface dataset in Meteorological Assimilation Data Ingest System (MADIS) were assimilated. This new method takes into account the observation errors of both wind speed (spd) and direction (dir), and WRFDA background quality control (BKG-QC) influences the choice of wind observations, due to data conversions between (u,v) and (spd, dir). The impacts of BKG-QC, as well as the new method, on the wind analysis were analysed separately. Because the dir observational errors produced by different platforms are not known or tuned well in WRFDA, a practical method, which uses similar assimilation weights in comparative trials, was employed to estimate the spd and dir observation errors. The asm_sd produces positive impacts on analyses and short-range forecasts of spd and dir with smaller root-mean-square errors than the u,v-based system. The bias of spd analysis decreases by 54.8%. These improvements result partly from BKG-QC screening of spd and dir observations in a direct way, but mainly from the independent impact of spd (dir) data assimilation on spd (dir) analysis, which is the primary distinction from the standard WRFDA method. The potential impacts of asm_sd on precipitation forecasts were evaluated. Results demonstrate that the asm_sd is able to indirectly improve the precipitation forecasts by improving the prediction accuracies of key wind-related factors leading to precipitation (e.g. warm moist advection and frontogenesis).

Atmospheric Motion Vectors from INSAT-3D: Initial quality assessment and its impact on track forecast of cyclonic storm NANAUK

The advanced Indian meteorological geostationary satellite INSAT-3D was launched on 26 July 2013 with an improved imager and an infrared sounder and is placed at 82°E over the Indian Ocean region. With the advancement in retrieval techniques of different atmospheric parameters and with improved imager data have enhanced the scope for better understanding of the different tropical atmospheric processes over this region. The retrieval techniques and accuracy of one such parameter, Atmospheric Motion Vectors (AMV) has improved significantly with the availability of improved spatial resolution data along with more options of spectral channels in the INSAT-3D imager. The present work is mainly focused on providing brief descriptions of INSAT-3D data and AMV derivation processes using these data. It also discussed the initial quality assessment of INSAT-3D AMVs for a period of six months starting from 01 February 2014 to 31 July 2014 with other independent observations: i) Meteosat-7 AMVs available over this region, ii) in-situ radiosonde wind measurements, iii) cloud tracked winds from Multi-angle Imaging Spectro-Radiometer (MISR) and iv) numerical model analysis. It is observed from this study that the qualities of newly derived INSAT-3D AMVs are comparable with existing two versions of Meteosat-7 AMVs over this region. To demonstrate its initial application, INSAT-3D AMVs are assimilated in the Weather Research and Forecasting (WRF) model and it is found that the assimilation of newly derived AMVs has helped in reduction of track forecast errors of the recent cyclonic storm NANAUK over the Arabian Sea. Though, the present study is limited to its application to one case study, however, it will provide some guidance to the operational agencies for implementation of this new AMV dataset for future applications in the Numerical Weather Prediction (NWP) over the south Asia region.

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

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.

Understanding the Influence of Assimilating Subsets of Enhanced Atmospheric Motion Vectors on Numerical Analyses and Forecasts of Tropical Cyclone Track and Intensity with an Ensemble Kalman Filter

Abstract Recent studies have shown that assimilating enhanced satellite-derived atmospheric motion vectors (AMVs) has improved mesoscale forecast of tropical cyclones (TC) track and intensity. The authors conduct data-denial experiments to understand where the TC analyses and forecasts benefit the most from the enhanced AMV information using an ensemble Kalman filter and the Weather Research and Forecasting Model. The Cooperative Institute for Meteorological Satellite Studies at the University of Wisconsin provides enhanced AMV datasets with higher density and temporal resolution using shorter-interval image triplets for the duration of Typhoon Sinlaku and Hurricane Ike (both 2008). These AMV datasets are then spatially and vertically subsetted to create six parallel cycled assimilation-forecast experiments for each TC: all AMVs; AMVs withheld between 100 and 350 hPa (upper layer), between 350 and 700 hPa (middle layer), and between 700 and 999 hPa (lower layer); and only AMVs within (interior) and outside (exterior) 1000-km radius of the TC center. All AMV subsets are found to be useful in some capacity. The interior and upper-layer AMVs are particularly crucial for improving initial TC position, intensity, and the three-dimensional wind structure along with their forecasts. Compared with denying interior or exterior AMVs, withholding AMVs in different tropospheric layers had less impact on TC intensity and size forecasts. The ensemble forecast is less certain (larger spread) in providing accurate TC track, intensity, and size when upper-layer AMVs or interior AMVs are withheld. This information could be useful to potential targeting scenarios, such as activating and focusing satellite rapid-scan operations, and decisions regarding observing system assessments and deployments.

Atmospheric Motion Vectors Height Assignment and Verification in Clear\|sky Regions from Infrared Imagery

Atmospheric Motion Vectors Height Assignment and Verification in Clear\|sky Regions from Infrared Imagery

Impact of Kalpana-1 retrieved multispectral AMVs on Mahasen tropical cyclone forecast

The atmospheric motion vectors (AMVs) retrieved from geostationary satellites are recognized as one of the important inputs for numerical weather prediction models to improve the tropical cyclone (TC) forecast. In this study, the weather research and forecasting (WRF) model, WRF three-dimensional variational (3D-Var) data assimilation system and WRF tangent linear and adjoint model are used to investigate the impact of multispectral Kalpana-1 AMVs on the simulation of Mahasen tropical cyclone (now known as cyclonic storm Viyaru) over the Indian Ocean. Three different sets of experiments are performed to evaluate the impact of Kalpana-1 AMVs. First, the impacts of Kalpana-1 AMVs are evaluated for different forecast lengths. The assimilation of Kalpana-1 AMVs improves the cyclone track prediction compared to control experiment. However, all the experiments are unable to capture the deep re-curvature of the TC. The next set of experiments is performed to evaluate the impact of Kalpana-1 AMVs derived from different multispectral channels (viz. visible, infrared and water vapor channels). More improvement is observed in TC track forecast when AMVs from water vapor channel are used for assimilation compared to infrared channel. Results also show degradation in short-range forecast when less-strict quality control is used for AMVs assimilation, but a considerable improvement is observed in long-range forecasts. Finally, the WRF tangent linear and adjoint model is used to compute the forecast sensitivity to Kalpana-1 AMVs observations. Upper- and lower-level circulation information provided by the Kalpana-1 AMVs influences the TC steering flow, and a positive impact on the track prediction is observed.