Atmospheric Motion Vectors Retrieval Research Articles

Evaluation of the EUMETSAT Global AVHRR Wind Product

AbstractThe European Organisation for the Exploitation of Meteorological Satellites (EUMETSAT) MetOp-A and MetOp-B satellites fly in the same polar orbit with a 180° phase difference, which enables the global retrieval of atmospheric motion vectors (AMVs, or “winds”) by tracking clouds in a pair of Advanced Very High Resolution Radiometer (AVHRR) infrared-window-channel images taken successively by the tandem platforms within their swath overlap area. This novel global wind product has been operational since 2015. As part of ongoing validation efforts, two months of MetOp global AMVs were compared with a suite of independent wind data, including AMVs from geostationary and polar-orbiter satellites as well as radiosonde and model winds. The performance of the new wind product is generally comparable to that of more established satellite winds. In the tropics, however, high-level MetOp global AMVs show a strong fast speed bias, increased root-mean-square difference, and considerably reduced speed correlation relative to all comparison datasets—an as-yet-unexplained drop in retrieval quality that warrants further investigation. A best-fit wind analysis also indicates that selectively applied height adjustments, such as cloud-base and inversion methods, can be a significant source of discrepancy, leading to very poor height correlation among low-level satellite AMVs. Height assignment is more consistent and better correlated at mid- to high levels, although MetOp heights derived from window-channel brightness temperatures have a bias toward lower heights because of the lack of semitransparency corrections. Collocated Infrared Atmospheric Sounding Interferometer CO2-slicing heights significantly improve the best-fit height-difference statistics at higher altitudes but are available for only ~5% of MetOp AMVs.

Impact of Kalpana-1 retrieved atmospheric motion vectors on mesoscale model forecast during summer monsoon 2011

The atmospheric motion vectors (AMVs) retrieved from multi-spectral geostationary satellites form a very crucial input to improve the initial conditions of numerical weather prediction (NWP) models at all operational agencies throughout the globe. With the recent update of operational AMV retrieval algorithm using infrared, water vapor, and visible channels of Indian geostationary meteorological satellite Kalpana-1, an attempt has been made to assess the impact of AMVs in the NWP models. In this study, the impact of Kalpana-1 AMVs is assessed by assimilating them in the Weather Research and Forecasting (WRF) model using three-dimensional variational data assimilation method during the entire month of July 2011 over the Indian Ocean region. Apart from Kalpana-1 AMVs, the other AMVs available from Global Telecommunications System (GTS) are also assimilated to generate the WRF model analyses. After the initial verification of WRF model analyses, the 12-h wind forecasts from the WRF model are compared with National Centers for Environmental Prediction Global Data Assimilation System final analyses. The assimilation of Kalpana-1 AMVs shows positive impact in 12-h wind forecast over the tropical region in the upper troposphere. Similar results are obtained when other AMVs available through GTS are used for assimilation, though the magnitude of positive impact of Kalpana-1 AMVs is slightly higher over tropical region. The 24-h rainfall forecasts are also improved over the Western India and the Bay of Bengal region, when Kalpana-1 AMVs are used for assimilation against control experiments.

Height Assignment Improvement in Kalpana-1 Atmospheric Motion Vectors

The real-time operational use of atmospheric motion vectors (AMVs) at numerical weather prediction (NWP) centers in India are being adversely affected due to inaccurate height assignment of cloud tracers, especially in thin semi-transparent clouds. In India, the operational derivation of AMVs from the Indian geostationary satellite Kalpana-1 began few years ago. A statistical empirical method (SEM) of height assignment, based on a genetic algorithm, is currently used to estimate the height of the retrieved vectors from Kalpana-1. This method has many limitations. In this paper, attempts have been made to implement the widely used and well tested height assignment methods such as the infrared window (WIN) technique, the H2O intercept, and the cloud base method in the Kalpana-1 AMV retrieval algorithm. The new height assignment algorithm significantly improves the statistics of the retrieved winds when compared to radiosondes, especially in high and mid levels winds.

Atmospheric motion vector retrieval using improved tracer selection algorithm

Tracer selection is the fundamental step in the retrieval of atmospheric motion vectors (AMVs). In this study, a new technique for tracer selection based on extracting the corner points in an infrared (IR) image of a geostationary satellite for the retrieval of AMVs is developed. Corner points are frequently used in computer vision to identify the important features of an image. These points are usually characterized by high gradient values of the image intensity in all directions and lie at the junctions of different brightness regions in the image. Corner points find application in computer vision for motion tracking, stereo vision, mosaics, etc., but this is the first time that the information from corners is used for tracer selection in AMV retrieval. In the present study, a commonly used Harris corner (HC) detection algorithm is followed to extract corners from the image intensity of an IR image. The tracers selected using the HC method are then passed on to the other steps of the retrieval algorithm, viz., tracking, height assignment, and quality control procedures for the retrieval of AMVs. For the initial development of the HC, Meteosat-7 IR images are used to derive AMVs for July and December 2010. The AMVs retrieved using HC are validated against collocated radiosonde observations, and the results are compared with the local anomaly (LA) method as reference. LA is used for tracer selection in operational AMV retrieval algorithm from the Indian geostationary satellite Kalpana-1. AMVs retrieved using HC have shown considerable improvement in the AMV accuracy over the AMVs derived using LA.

Assessment of a new quality control technique in the retrieval of atmospheric motion vectors

Atmospheric motion vectors (AMVs) are generally derived using three successive images of a geosyn- chronous satellite in most of the operational centres in world. This study presents a new quality control technique used during the retrieval of AMVs from the images of infrared and water vapour channels of the Indian geostationary satellite Kalpana-1. The new technique proposed here is based on wind retrieval using the previous eight images. The AMVs retrieved using a new algorithm and the old algorithm that uses the automatic quality control procedure of European Organisation for the Exploitation of Meteorological Satellites (EUMETSAT), are compared with collocated radiosonde data and numerical weather prediction model analysis. The new algorithm shows an improvement over the old one at all levels both in terms of root mean square vector difference and spatial pattern. It has also produced significantly larger numbers of valid retrievals.

Image registration and atmospheric motion vectors retrieval from Geo-stationary satellite

In the determination of atmospheric motion vectors (AMVs) from sequential images obtained from geostationary satellites, registration of the images play a primary and important role. Image registration is an essential and fundamental component in the retrieval of AMVs from triplet consecutive images of Kalpana -1 satellite is done by suitable matching of valid tracers in back and forth from middle image. If the triplet is not properly registered than it may lead to errors in wind speed and direction. The inaccuracy of registration (N-S or E –W shift) in one set of triplet images will generate the errors in wind speed and direction this will affect in other images if available in sequence also. Image registration maintains the spatial relationship between the pixels within images and between images. Improper registration results due to the deviation in orbital parameters, spacecraft attitude, thermal distortions and earth sensor biases. If we need continuous train of images like, sometimes we need morphing in images to get continuous AMVs which can be a potential source of errors for the input of Numerical Weather Prediction (NWP). But in this framework the other issues will also need further investigation like cloud evolution, height assignment and thickness biases, etc. in AMVs. In this paper, authors will deal only the registration issue. It has been shown from daily registration shift in Kalpana -1 satellite images during the year 2008 between 0000UTC to 0200 UTC in Northern Hemisphere that, the errors introduced in wind speed varies from 10 m/sec to 45 m/sec at nadir due to registration only. Few cases have been shown appreciable improvement after applying the interactive correction of registration errors. Keywords: AMV, Image registration, NWP, Weather

Quality control issues in atmospheric motion vectors

Currently, atmospheric motion vectors (AMVs) are hourly generated satellite derived product on operational basis at India Meteorological Department (IMD), New Delhi. These wind vectors are associated with errors and difficult to use in mesoscale models without considering the quality issues. Quality control is an integral part of the AMVs retrieval from geo-stationary satellites. Present paper deals with various quality indicator or flags used in Automatic Quality Control (AQC) scheme of European Organization for the Exploitation of Meteorological Satellites (EUMETSAT) with new coefficients and Auto Editor (AE) criterion at Cooperative Institute for Meteorological Satellite Studies (CIMSS) on Kalpana -1 satellite data. The results obtained from the new parameters in AQC and with height adjustment made with AE are more realistic and free from spurious winds in both Infrared (IR) and Water Vapour (WV) channels. Collocation with radiosonde during one month period shows an average decrease (decrease) of RMSE in CMVs (WVWs) is of the order of 4 % (3 %) and increase (decrease) in mean bias is of the order of 3 % (10 %).