Atmospheric Motion Vectors Research Articles

Subtropical Cyclone Formation via Warm Seclusion Development: The Importance of Surface Fluxes

AbstractSubtropical cyclones (STCs) are characterized by a thermal hybrid structure with tropical and extratropical features. STCs are considered a numerical modeling challenge because of their rapid intensification. A fundamental part of their strength is derived from diabatic processes associated with convection and heat fluxes from the ocean. This study evaluates the importance of surface turbulent heat fluxes during the transition of an extratropical precursor into a STC. This cyclone evolved embedded within a strong meridional flow, having a Shapiro‐Keyser structure and undergoing a warm seclusion process. To assess the importance of those heat fluxes, two Weather Research and Forecasting simulations were defined considering the presence and absence of those fluxes. Results of both simulations reveal a warm seclusion process, which weakened in absence of the heat fluxes. During the system genesis and in absence of heat fluxes, the wind and rainfall values were increased due to the remarkably intense area of frontogenesis to the northwest. Given these results and the lack of transition in the absence of heat fluxes, the frontal nature of the system was verified. Considering the heat fluxes, the obtained potential vorticity values diminished, reducing wind shear and intensifying convection in the system, which favored its transition into an STC. This study is groundbreaking in that no STC has been linked to a warm seclusion process in the Eastern North Atlantic. Additionally, simulated wind field shows an underestimation in comparison with Atmospheric Motion Vectors, used as observational data so as to give a weight to the wind analysis.

Improving Hurricane Analyses and Predictions with TCI, IFEX Field Campaign Observations, and CIMSS AMVs Using the Advanced Hybrid Data Assimilation System for HWRF. Part II: Observation Impacts on the Analysis and Prediction of Patricia (2015)

Abstract Diverse observations, such as the High Definition Sounding System (HDSS) dropsonde observations from the Tropical Cyclone Intensity (TCI) program, the Tail Doppler Radar (TDR), Stepped Frequency Microwave Radiometer (SFMR), and flight-level observations from the Intensity Forecasting Experiment (IFEX) program, and the atmospheric motion vectors (AMVs) from the Cooperative Institute for Meteorological Satellite Studies (CIMSS) simultaneously depicted the three-dimensional (3D) structure of Hurricane Patricia (2015). Experiments are conducted to understand the relative impacts of each of these observation types on Patricia’s analysis and prediction using the Gridpoint Statistical Interpolation (GSI)-based ensemble-variational data assimilation system for the Hurricane Weather Research and Forecasting (HWRF) Model. In comparing the impacts of assimilating each dataset individually, results suggest that 1) the assimilation of 3D observations produces better TC structure analysis than the assimilation of two-dimensional (2D) observations; 2) the analysis from assimilating observations collected from platforms that only sample momentum fields produces a less improved forecast with either short-lived impacts or slower intensity spinup as compared to the forecast produced after assimilating observations collected from platforms that sample both momentum and thermal fields; and 3) the structure forecast tends to benefit more from the assimilation of inner-core observations than the corresponding intensity forecast, which implies better verification metrics are needed for future TC forecast evaluation.

A Brief Assessment of the Impact of Nearly 40 Years of Assimilated Observations Over the Amazon Basin

AbstractAdding a Forecast Sensitivity‐based Observation Impact component to Version 2 of the Modern Era Retrospective‐analysis for Research and Applications, the present study provides an assessment of the impact of nearly 40 years of observations on short‐range (24‐hr) forecasts over the Amazon basin. Under self‐verification, forecast errors are found to slightly increase from the early data‐sparse days to the more recent years, when data dramatically increase. Throughout the reanalysis, satellite radiances dominate in volume, but only before 1999 they dominate the impacts. Beyond 1999, over 50% of forecast error reduction is associated with conventional observations (radiosondes). Atmospheric Motion Vectors are also found to be large contributors to error reduction, but their contribution reduces in dry periods. In opposition to Atmospheric Motion Vectors, satellite radiances tend to contribute more in the dry season. Results provide motivation for additional conventional observations and the use of all‐sky treatment of radiances.

Simulation of Atmospheric Motion Vectors for an Observing System Simulation Experiment

AbstractAn algorithm to simulate locations of atmospheric motion vectors for use in observing system simulation experiments is described and demonstrated. It is intended to obviate likely deficiencies in nature run data if used to produce images for feature tracking. The algorithm employs probabilistic functions that are tuned based on distributions of real observations and histograms of nature run fields. For distinct observation types, the algorithm produces geographical and vertical distributions, time-mean counts, and typical spacings of simulated locations that are, at least qualitatively, similar to those of real observations and are associated with nature run cloud and water vapor fields. It thus appears suitable for generating realistic atmospheric motion vectors for use in observing system simulation experiments.

Impact of Rapid‐Scan‐Based Dynamical Information From GOES‐16 on HWRF Hurricane Forecasts

AbstractObservations of dynamical information in the upper levels of tropical cyclones at high spatiotemporal resolutions are rare but very important to the analysis and prediction of the storm evolution and landfall impacts. These observations are now becoming routinely available from the new generation of geostationary weather satellites. Understanding and optimizing the utilization of that information in numerical weather prediction models is a vital step toward simulating tropical cyclone behavior and improving forecasts. The Advanced Baseline Imager (ABI) onboard GOES‐16 is providing high spatial and temporal resolution images that can be targeted on North Atlantic tropical cyclones. In addition to a full‐disk scan every 10 min and a CONUS scan every 5 min, the ABI also has a flexible “mesoscale scan” mode featuring limited moving domains at 1‐min intervals. The mesosector can focus on a targeted storm center with a 10°×10° domain coverage that follows the storm movement. Using this 1‐min ABI imagery to track cloud motions, automated algorithms have been developed to produce enhanced, high‐resolution atmospheric motion vectors (AMVs) during a targeted tropical cyclone event. These high spatiotemporal AMVs represent estimates of the wind field around the storm and can provide critical dynamical information on the targeted storm and its near environment. This information can help improve the representation of the initialized vortex in numerical model analyses. To study the impact of the enhanced AMV observations on numerical weather prediction, the Hurricane Weather Research Forecast (HWRF) model is used in a series of assimilation and forecast experiments. Three destructive Atlantic hurricane cases from 2017, Harvey, Irma, and Maria, are chosen as case studies. The results show that the assimilation of the enhanced AMVs from GOES‐16 consistently improves the HWRF hurricane track and size forecasts, and have mixed impacts on intensity forecasts. These results augment previously published studies on optimizing the quantitative use of new generation geostationary satellite rapid‐scan observations for improving high impact weather forecasts.

Retrieval and applications of atmospheric motion vectors derived from Indian geostationary satellites INSAT-3D/INSAT-3DR

The atmospheric motion vectors (AMVs) from Indian geostationary meteorological satellites are derived operationally at Space Applications Centre (SAC), Ahmedabad. Over the years, many improvements in the operational retrieval algorithm have taken place with the advancement in the retrieval techniques as well as in the sensors. Two Indian geostationary meteorological satellites INSAT-3D and INSAT-3DR launched on 26 July 2013 (placed at 82° E) and 08 September 2016 (placed at 74° E), respectively, are providing continuous coverage at every 15 min. The data from these satellites have enhanced the scope for better understanding of atmospheric processes over the Indian Ocean region. The retrieval techniques and accuracy of AMVs have improved significantly with improved spatial resolution data along with more number of spectral channels available in both satellites. In this work, quality assessment of operational AMVs retrieved using four different spectral channels (visible, mid-infrared, infrared and water vapour) of both the satellites at 4-km spatial resolution is discussed. It also discusses the retrieval of staggering AMVs with simultaneous usage of INSAT-3D and INSAT-3DR data. The derivation of different atmospheric parameters (viz. convergence, divergence, vorticity, wind shear etc.) using AMVs of both the satellites and their possible applications during tropical cyclogenesis are also discussed. Recently, the algorithm for the retrieval of high-resolution (HR) visible AMVs and rapid-scan (RS) AMV using INSAT-3DR data has been developed and tested; however, these new algorithms are yet to be implemented operationally. To demonstrate the initial applications, AMVs are assimilated into numerical model to assess their impact for forecast improvement. The quality and impact assessment from this study will provide useful guidance to the operational meteorological agencies for applications of existing and new INSAT-3D and INSAT-3DR AMV data for future applications in the numerical weather prediction (NWP) model over the South Asian region.

Assessment of INSAT-3D/INSAT-3DR Derived Atmospheric Motion Vectors Using Wind Profiler Observations Located at Gadanki, India

The accurate estimation of atmospheric motion vectors (AMVs) derived using sequences of images from geostationary satellites is very important because of their extensive use in improving initial conditions of the numerical weather prediction (NWP) models. To assess the quality of retrieved AMVs, validation of these winds with ground-based observation is very important. In the present study, the evaluation of AMVs retrieved using the data from geostationary satellites, viz. INSAT-3D/INSAT-3DR, has been carried out with respect to wind profiler data and radiosonde data. The wind profiler used in the present study is an L-band radar lower atmosphere wind profiler and is installed at the National Atmospheric Research Laboratory, Gadanki, located at 13.27° N, 79.10° E. This instrument measures continuous high-resolution wind measurements in the lower atmosphere. The radiosonde data available near Gadanki are used for validation. In this study, normal infrared (IR) AMV derived using 10.5–11.5 μm channel of INSAT-3D and INSAT-3DR satellites and staggering-mode IR AMV retrieved using INSAT-3D/INSAT-3DR combination are used. AMV accuracy assessment was performed for the years 2017 and 2018 by calculating statistical scores, viz. root mean square vector difference, wind speed bias and a number of matching points. In addition, the INSAT-3D/INSAT-3DR AMV and profiler wind data are also used to study the characteristics of low-level jet during active and break period of the Indian summer monsoon of 2017 and 2018. The accuracy assessment through this study shows the usefulness of AMVs for improving the initial conditions in NWP models in future.

Improvements for Atmospheric Motion Vectors Algorithm Using First Guess by Optical Flow Method

Improvements for Atmospheric Motion Vectors Algorithm Using First Guess by Optical Flow Method

Inter-comparison of INSAT-3D atmospheric motion vectors height with cloud-base height from a Ceilometer

ABSTRACTThe atmospheric motion vectors (AMV) are derived by tracking cloud and moisture features in the subsequent images of geostationary as well as polar satellites. The heights of the AMVs are nothing but the height of cloud tracers used during the retrieval process for tracking. This height is derived using different complex techniques. In this study, a detailed comparison has been performed with the use of ground-based cloud-base height (CBH) measurements from ceilometer CL31, installed at Ahmedabad (23.03°N, 72.54°E), India and height assigned to AMVs which are retrieved from INSAT-3D satellite images. Six months CBH measurement over Ahmedabad from ceilometer CL31 has been used to inter-compare the co-located AMV heights. Although both ground-based and satellite-based techniques have their own limitations, however, it is found from this study that the ceilometer is an excellent instrument to precisely detect low- and mid-level clouds and height-assignments technique of AMVs retrieved from INSAT-3D satellite provides all high-, mid- and low-levels cloud information over this region. As an example, it is found that AMVs height of INSAT-3D is about 867.92, 750.00 and 465.09 hPa on 26 May 2014, 7 July 2014 and 29 October 2014, respectively, which matches very closely with ceilometer-measured CBH of about 873.15, 769.16 and 507.44 hPa, respectively. However, in case multi-level clouds present on rainy days, CBH measurements from ceilometer are differing from INSAT-3D AMV cloud tracer heights.

An Experiment for Assimilating Different Type of Data Observations in Forecasting Heavy Rainfall over Central Highlands Region Due to the Impact of Hurricane Damrey

This study investigates and assesses the impact of assimilating data types (observed data surface, sounding, and satellite-derived atmospheric motion vectors – AMVs) for the Weather Research and Forecasting (WRF) in forecasting heavy rainfall over Central Highlands region, due to the impact of hurricane Damrey. The WRF model combined with the Gridpoint Statistical Interpolation (GSI) system, was started running at 12Z 03/11/2017, and 84h forecasts in advance, with two kinds for running assimilation "cold start" and "warm start", and with the three-dimensional variational data assimilation (3D-Var) method. The results showed that assimilated cases have improved forecasting about spatial distribution and amount of rainfall at a 24-hour lead time, in which, the "warm start" for better forecasting. Notably, the assimilation of AMVs data with the "warm start" run has improved forecasting quality of heavy rainfall, the POD, FAR, and CSI indicators are the best at the 24-hour lead time, for rainfall thresholds greater than 80mm.
 

Demonstration and Evaluation of 3D Winds Generated by Tracking Features in Moisture and Ozone Fields Derived from AIRS Sounding Retrievals

For more than 15 years, polar winds from the Moderate Resolution Imaging Spectroradiometer (MODIS) imagery have been generated by the National Oceanic and Atmospheric Administration (NOAA) and the Cooperative Institute for Meteorological Satellite Studies (CIMSS). These datasets are a NOAA National Environmental Satellite, Data, and Information Service (NESDIS) operational satellite product that is used at more than 10 major numerical weather prediction (NWP) centers worldwide. The MODIS polar winds product is composed of both infrared window (IR-W) and water vapor (WV) tracked features. The WV atmospheric motion vectors (AMV) yield a better spatial distribution than the IR-W since both cloud and clear-sky features can be tracked in the WV images. As the new generation polar satellite-era begins with the Suomi National Polar-orbiting Partnership (S-NPP), there is currently no WV channel on the Visible/Infrared Imager/Radiometer Suite (VIIRS), resulting in a data gap with only IR-W derived AMVs possible. This scenario presents itself as an opportunity to evaluate hyperspectral infrared moisture retrievals from consecutive overlapping satellite polar passes to extract atmospheric motion from clear-sky regions on constant (and known) pressure surfaces, i.e., estimating winds in retrieval space rather than radiance space. Perhaps most significantly, this method has the potential to provide vertical wind profiles, as opposed to the current MODIS-derived single-level AMVs. In this study, the winds technique is applied to Atmospheric Infrared Sounder (AIRS) moisture retrievals from NASA’s Aqua satellite. The resulting winds are assimilated into the Goddard Earth Observing System Model, Version 5 (GEOS-5). The results are encouraging, as the AIRS retrieval polar AMVs have a similar quality as the MODIS AMVs and exhibit a positive impact in the hemispheric Day 4.5 to 6.5 forecasts for a one-month experiment in July 2012.

Quantitative Assessment of State-Dependent Atmospheric Motion Vector Uncertainties

AbstractThis study examines the error characteristics of atmospheric motion vectors (AMVs) obtained by tracking the movement of water vapor features. A high-resolution numerical simulation of a dynamic weather event is used as a baseline, and AMVs tracked from retrieved water vapor fields are compared with the “true” winds produced by the model. The sensitivity of AMV uncertainty to time interval, AMV tracking window size, water vapor content, horizontal gradient, and wind structure is examined. AMVs are derived from the model water vapor field at a specific height and also from water vapor fields vertically blurred using smoothing functions consistent with high-spectral-resolution infrared (IR) and high-frequency microwave (MW) water vapor sounders. Uncertainties in water vapor AMVs are state dependent and are largest for regions with small water vapor content and small water vapor spatial gradient and in places where the flow runs parallel to contours of constant water vapor content. Smoothing of water vapor consistent with IR and MW retrievals does not increase AMV uncertainty; however, the yield of AMVs from IR sounders is much lower than from MW sounders because of the inability of IR sounders to retrieve water vapor below clouds. The yield and error are similar for AMVs in the lower and upper troposphere, even though the water vapor content in the upper troposphere is much smaller. The results have implications for the design of new observing systems, as well as the specification of errors when AMVs are ingested in data assimilation systems.

Impacts of Assimilating High-Resolution Atmospheric Motion Vectors Derived from Himawari-8 on Tropical Cyclone Forecast in HWRF

AbstractThe impact of the assimilation of high spatial and temporal resolution atmospheric motion vectors (AMVs) on tropical cyclone (TC) forecasts has been investigated. The high-resolution AMVs are derived from the full disk scan of the new generation geostationary satellite Himawari-8. Forecast experiments for three TCs in 2016 in a western North Pacific basin are performed using the National Centers for Environmental Prediction (NCEP) operational Hurricane Weather Research and Forecasting Model (HWRF). Two different ensemble–variational hybrid data assimilation configurations (using background error covariance created by global ensemble forecast and HWRF ensemble forecast), based on the Gridpoint Statistical Interpolation (GSI), are used for the sensitivity experiments. The results show that the inclusion of high-resolution Himawari-8 AMVs (H8AMV) can benefit the track forecast skill, especially for long-range lead times. The diagnosis of optimal steering flow indicates that the improved track forecast seems to be attributed to the improvement of initial steering flow surrounding the TC. However, the assimilation of H8AMV increases the negative intensity bias and error, especially for short-range forecast lead times. The investigation of the structural change from the assimilation of H8AMV revealed that the following two factors are likely related to this 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. Assimilating H8AMV using background error covariance created from HWRF ensemble forecast contributes to a significant reduction in negative intensity bias and error, and there is a significant benefit to TC size forecast.

2018 Atmospheric Motion Vector (AMV) Intercomparison Study

Atmospheric Motion Vectors (AMVs) calculated by six different institutions (Brazil Center for Weather Prediction and Climate Studies/CPTEC/INPE, European Organization for the Exploitation of Meteorological Satellites/EUMETSAT, Japan Meteorological Agency/JMA, Korea Meteorological Administration/KMA, Unites States National Oceanic and Atmospheric Administration/NOAA, and the Satellite Application Facility on Support to Nowcasting and Very short range forecasting/NWCSAF) with JMA’s Himawari-8 satellite data and other common input data are here compared. The comparison is based on two different AMV input datasets, calculated with two different image triplets for 21 July 2016, and the use of a prescribed and a specific configuration. The main results of the study are summarized as follows: (1) the differences in the AMV datasets depend very much on the ‘AMV height assignment’ used and much less on the use of a prescribed or specific configuration; (2) the use of the ‘Common Quality Indicator (CQI)’ has a quantified skill in filtering collocated AMVs for an improved statistical agreement between centers; (3) Among the six AMV operational algorithms verified by this AMV Intercomparison, JMA AMV algorithm has the best overall performance considering all validation metrics, mainly due to its new height assignment method: ‘Optimal estimation method considering the observed infrared radiances, the vertical profile of the Numerical Weather Prediction wind, and the estimated brightness temperature using a radiative transfer model’.

CubeSat Infrared Atmospheric Sounder technology development status

Hyperspectral infrared sounding in a CubeSat will provide a new dimension to the current suite of IR sounders by allowing measurements at multiple times of day and enabling formation flying of IR sounders for new data products such as atmospheric motion vector winds. We focus on technology development during the CubeSat Infrared Atmospheric Sounder (CIRAS) project sponsored by the NASA Earth Science Technology Office (ESTO) and coincident studies by the National Oceanic and Atmospheric Administration (NOAA) Office of Projects, Planning, and Analysis (OPPA). The CIRAS approach incorporates key instrument technologies, developed at the Microdevices Lab (MDL) at Jet Propulsion Laboratory (JPL), including a two-dimensional array of High Operating Temperature Barrier Infrared Detector (HOT-BIRD) material, selected for its high uniformity, low cost, low noise, and higher operating temperatures than traditional materials. The second key technology is a mid-wavelength infrared grating spectrometer designed by Ball Aerospace with a JPL MDL slit and immersion grating to provide hyperspectral infrared imaging in a CubeSat volume. The third key technology is a blackbody calibration target fabricated with MDL’s black silicon to have very high emissivity in a flat plate construction. JPL has completed design and breadboard of the mechanical, electronic, and thermal subsystems for the CIRAS payload including a HOT-BIRD focal plane assembly, with filters in a dewar and a breadboard of the electronics and scan mirror assembly. Blue Canyon Technologies, developer of the CIRAS 6U CubeSat, completed the Final Design Review for the spacecraft. NOAA is sponsoring the continued development of the CIRAS Proto-Flight Model (PFM) instrument at JPL using many of the existing subsystems. Completion of the PFM is expected in mid 2021, with launch no earlier than 2022.

Concept Development and Risk Reduction for MISTiC Winds, A Micro-Satellite Constellation Approach for Vertically Resolved Wind and IR Sounding Observations in the Troposphere

MISTiC Winds is an instrument and constellation mission approach to simultaneously observe the global thermodynamic state and the vertically resolved horizontal wind field in the troposphere from LEO SSO. The instrument is a wide-field imaging spectrometer operated in the 4.05–5.75 μm range, with the spectral resolution, sampling, radiometric sensitivity, and stability needed to provide temperature and water vapor soundings of the atmosphere, with 1 km vertical resolution in the troposphere-comparable to those of NASA’s atmospheric infrared sounder (AIRS). These instruments have much higher spatial resolution (<3 km at nadir) and finer spatial sampling than current hyperspectral sounders, allowing a sequence of such observations from several micro-satellites in an orbital plane with short time separation, from which atmospheric motion vector (AMV) winds are derived. AMVs for both cloud-motion and water vapor-motion, derived from hyperspectral imagery, will have improved velocity resolution relative to AMVs obtained from multi-spectral instruments operating in GEO. MISTiC’s extraordinarily small size, low mass (<15 kg), and minimal cooling requirements can be accommodated aboard an ESPA-class microsatellite. Low fabrication and launch costs enable this constellation to provide more frequent atmospheric observations than current-generation sounders provide, at much lower mission cost. Key technology and observation method risks have been reduced through recent laboratory and airborne (NASA ER2) testing funded under NASA’s Instrument Incubator Program and BAE Systems IR&D, and through an observing system simulation experiment performed by NASA GMAO. This approach would provide a valuable new capability for the study of the processes driving high-impact weather events, and critical high-resolution observations needed for future numerical weather prediction.

Winds of Change for Future Operational AMV at EUMETSAT

EUMETSAT, the European Organization for the Exploitation of Meteorological Satellites, is one of the key contributors to global atmospheric motion vector (AMV) production around the world. Its current contribution includes geostationary satellites at 0.0 and 41.5 degrees east, and several products extracted from the Metop low-orbit satellites. These last ones mainly cover high-latitude regions completing the observations from the geostationary ring. In the upcoming years, EUMETSAT will launch a new generation of geostationary and low-orbit satellites. The imager instruments Flexible Combined Imager (FCI) and METImage will take over the nominal AMV production at EUMETSAT around 2022 and 2024. The enhanced characteristics of these new-generation instruments are expected to increase AMV production and to improve the quality of the products. This paper presents an overview of the current EUMETSAT AMV operational production, together with a roadmap of the preparation activities for the new generation of satellites. The characteristics of the upcoming AMV products are described and compared to the current operational AMV products. This paper also presents a recent investigation into AMV extraction using the Sentinel-3 Sea and Land Surface Temperature Radiometer (SLSTR) instrument, as well as the retrieval of wind profiles from infrared sounders.

Joint 3D-Wind Retrievals with Stereoscopic Views from MODIS and GOES

Atmospheric motion vectors (AMVs), derived by tracking patterns, represent the winds in a layer characteristic of the pattern. AMV height (or pressure), important for applications in atmospheric research and operational meteorology, is usually assigned using observed IR brightness temperatures with a modeled atmosphere and can be inaccurate. Stereoscopic tracking provides a direct geometric height measurement of the pattern that an AMV represents. We extend our previous work with multi-angle imaging spectro–radiometer (MISR) and GOES to moderate resolution imaging spectroradiometer (MODIS) and the GOES-R series advanced baseline imager (ABI). MISR is a unique satellite instrument for stereoscopy with nine angular views along track, but its images have a narrow (380 km) swath and no thermal IR channels. MODIS provides a much wider (2330 km) swath and eight thermal IR channels that pair well with all but two ABI channels, offering a rich set of potential applications. Given the similarities between MODIS and VIIRS, our methods should also yield similar performance with VIIRS. Our methods, as enabled by advanced sensors like MODIS and ABI, require high-accuracy geographic registration in both systems but no synchronization of observations. AMVs are retrieved jointly with their heights from the disparities between triplets of ABI scenes and the paired MODIS granule. We validate our retrievals against MISR-GOES retrievals, operational GOES wind products, and by tracking clear-sky terrain. We demonstrate that the 3D-wind algorithm can produce high-quality AMV and height measurements for applications from the planetary boundary layer (PBL) to the upper troposphere, including cold-air outbreaks, wildfire smoke plumes, and hurricanes.

Development and Intercomparison Study of an Atmospheric Motion Vector Retrieval Algorithm for GEO-KOMPSAT-2A

We derived an atmospheric motion vector (AMV) algorithm for the Geostationary Korea Multipurpose Satellite (GEO-KOMPSAT-2A; GK-2A) launched on 4 December 2018, using the Advanced Himawari Imager (AHI) onboard Himawari-8, which is very similar to the Advanced Meteorological Imager onboard GK-2A. This study clearly describes the main steps in our algorithm and optimizes it for the target box size and height assignment methods by comparing AMVs with numerical weather prediction (NWP) and rawinsonde profiles for July 2016 and January 2017. Target box size sensitivity tests were performed from 8 × 8 to 48 × 48 pixels for three infrared channels and from 16 × 16 to 96 × 96 pixels for one visible channel. The results show that the smaller box increases the speed, whereas the larger one slows the speed without quality control. The best target box sizes were found to be 16 × 16 for CH07, 08, and 13, and 48 × 48 pixels for CH03. Height assignment sensitivity tests were performed for several methods, such as the cross-correlation coefficient (CCC), equivalent blackbody temperature (EBBT), infrared/water vapor (IR/WV) intercept, and CO2 slicing methods for a cloudy target as well as normalized total contribution (NTC) and normalized total cumulative contribution (NTCC) for a clear-air target. For a cloudy target, the CCC method is influenced by the quality of the cloud’s top pressure. Better results were found when using EBBT and IR/WV intercept methods together rather than individually. Furthermore, CO2 slicing had the best statistics. For a clear-air target, the combined use of NTC and NTCC had the best statistics. Additionally, the mean vector difference, root-mean-square (RMS) vector difference, bias, and RMS error (RMSE) between GK-2A AMVs and NWP or rawinsonde were smaller by approximately 18.2% on average than in the case of the Communication, Ocean and Meteorology Satellite (COMS) AMVs. In addition, we verified the similarity between GK-2A and Meteosat Third Generation (MTG) AMVs using the AHI of Himawari-8 from 21 July 2016. This similarity can provide evidence that the GK-2A algorithm works properly because the GK-2A AMV algorithm borrows many methods of the MTG AMV algorithm for geostationary data and inversion layer corrections. The Pearson correlation coefficients in the speed, direction, and height of the prescribed GK-2A and MTG AMVs were larger than 0.97, and the corresponding bias/RMSE were0.07/2.19 m/s, 0.21/14.8°, and 2.61/62.9 hPa, respectively, considering common quality indicator with forecast (CQIF) > 80.

NWCSAF High Resolution Winds (NWC/GEO-HRW) Stand-Alone Software for Calculation of Atmospheric Motion Vectors and Trajectories

The High Resolution Winds (NWC/GEO-HRW) software is developed by the EUMETSAT Satellite Application Facility on Support to Nowcasting and Very Short Range Forecasting (NWCSAF). It is part of a stand-alone software package for the calculation of meteorological products with geostationary satellite data (NWC/GEO). NWCSAF High Resolution Winds provides a detailed calculation of Atmospheric Motion Vectors (AMVs) and Trajectories, locally and in near real time, using as input geostationary satellite image data, NWP model data, and OSTIA sea surface temperature data. The whole NWC/GEO software package can be obtained after registration at the NWCSAF Helpdesk, www.nwcsaf.org, where users also find support and help for its use. NWC/GEO v2018.1 software version, available since autumn 2019, is able to process MSG, Himawari-8/9, GOES-N, and GOES-R satellite series images, so that AMVs and trajectories can be calculated all throughout the planet Earth with the same algorithm and quality. Considering other equivalent meteorological products, in the ‘2014 and 2018 AMV Intercomparison Studies’ NWCSAF High Resolution Winds compared very positively with six other AMV algorithms for both MSG and Himawari-8/9 satellites. Finally, the Coordination Group for Meteorological Satellites (CGMS) recognized in its ‘2012 Meeting Report’: (1) NWCSAF High Resolution Winds fulfills the requirements to be a portable stand-alone AMV calculation software due to its easy installation and usability. (2) It has been successfully adopted by some CGMS members and serves as an important tool for development. It is modular, well documented, and well suited as stand-alone AMV software. (3) Although alternatives exist as portable stand-alone AMV calculation software, they are not as advanced in terms of documentation and do not have an existing Helpdesk.