scholarly journals Application of Satellite-Derived Atmospheric Motion Vectors for Estimating Mesoscale Flows

2005 ◽  
Vol 44 (11) ◽  
pp. 1761-1772 ◽  
Author(s):  
Kristopher M. Bedka ◽  
John R. Mecikalski
Keyword(s):  
Deep Convection ◽  
Target Selection ◽  
Fold Increase ◽  
Synoptic Scale ◽  
Motion Vectors ◽  
Atmospheric Motion ◽  
Flow Components ◽  
Nwp Model ◽  
Atmospheric Motion Vectors ◽  
The Impact

Abstract This study demonstrates methods to obtain high-density, satellite-derived atmospheric motion vectors (AMV) that contain both synoptic-scale and mesoscale flow components associated with and induced by cumuliform clouds through adjustments made to the University of Wisconsin—Madison Cooperative Institute for Meteorological Satellite Studies (UW-CIMSS) AMV processing algorithm. Operational AMV processing is geared toward the identification of synoptic-scale motions in geostrophic balance, which are useful in data assimilation applications. AMVs identified in the vicinity of deep convection are often rejected by quality-control checks used in the production of operational AMV datasets. Few users of these data have considered the use of AMVs with ageostrophic flow components, which often fail checks that assure both spatial coherence between neighboring AMVs and a strong correlation to an NWP-model first-guess wind field. The UW-CIMSS algorithm identifies coherent cloud and water vapor features (i.e., targets) that can be tracked within a sequence of geostationary visible (VIS) and infrared (IR) imagery. AMVs are derived through the combined use of satellite feature tracking and an NWP-model first guess. Reducing the impact of the NWP-model first guess on the final AMV field, in addition to adjusting the target selection and vector-editing schemes, is found to result in greater than a 20-fold increase in the number of AMVs obtained from the UW-CIMSS algorithm for one convective storm case examined here. Over a three-image sequence of Geostationary Operational Environmental Satellite (GOES)-12 VIS and IR data, 3516 AMVs are obtained, most of which contain flow components that deviate considerably from geostrophy. In comparison, 152 AMVs are derived when a tighter NWP-model constraint and no targeting adjustments were imposed, similar to settings used with operational AMV production algorithms. A detailed analysis reveals that many of these 3516 vectors contain low-level (100–70 kPa) convergent and midlevel (70–40 kPa) to upper-level (40–10 kPa) divergent motion components consistent with localized mesoscale flow patterns. The applicability of AMVs for estimating cloud-top cooling rates at the 1-km pixel scale is demonstrated with excellent correspondence to rates identified by a human expert.

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 GEO-GEO Stereo-Tracking of Atmospheric Motion Vectors (AMVs) from the Geostationary Ring

2020 ◽  
Author(s):  
James L. Carr ◽  
Dong L. Wu ◽  
Jaime Daniels ◽  
Mariel D. Friberg ◽  
Wayne Bresky ◽  
...  
Keyword(s):  
Deep Convection ◽  
Motion Vectors ◽  
Fire Smoke ◽  
Atmospheric Motion ◽  
Image Navigation ◽  
Boundary Layer Dynamics ◽  
Vantage Points ◽  
Height Assignment ◽  
Atmospheric Motion Vectors ◽  
Geostationary Ring

Height assignment is an important problem for satellite measurements of Atmospheric Motion Vectors (AMVs) that are interpreted as winds by forecast and assimilation systems. Stereo methods assign heights to AMVs from the parallax observed between observations from different vantage points in orbit while tracking cloud or moisture features. In this paper, we fully develop the stereo method to jointly retrieve wind vectors with their geometric heights from geostationary satellite pairs. Synchronization of observations between observing systems is not required. NASA and NOAA stereo-winds codes have implemented this method and we have processed large datasets from GOES-16, -17, and Himawari-8. Our retrievals are validated against rawinsonde observations and demonstrate the potential to improve forecast skill. Stereo winds also offer an important mitigation for the loop heat pipe anomaly on GOES-17 during times when warm focal plane temperatures cause infra-red channels that are needed for operational height assignments to fail. We also examine several application areas, including deep convection in tropical cyclones, planetary boundary layer dynamics, and fire smoke plumes, where stereo methods provide insights into atmospheric processes. The stereo method is broadly applicable across the geostationary ring where systems offering similar Image Navigation and Registration (INR) performance as GOES-R are deployed.

scholarly journals GEO-GEO Stereo-Tracking of Atmospheric Motion Vectors (AMVs) from the Geostationary Ring

2020 ◽  
Author(s):  
James L. Carr ◽  
Dong L. Wu ◽  
Jaime Daniels ◽  
Mariel D. Friberg ◽  
Wayne Bresky ◽  
...  
Keyword(s):  
Deep Convection ◽  
Motion Vectors ◽  
Fire Smoke ◽  
Atmospheric Motion ◽  
Image Navigation ◽  
Boundary Layer Dynamics ◽  
Vantage Points ◽  
Height Assignment ◽  
Atmospheric Motion Vectors ◽  
Geostationary Ring

Height assignment is an important problem for satellite measurements of Atmospheric Motion Vectors (AMVs) that are interpreted as winds by forecast and assimilation systems. Stereo methods assign heights to AMVs from the parallax observed between observations from different vantage points in orbit while tracking cloud or moisture features. In this paper, we fully develop the stereo method to jointly retrieve wind vectors with their geometric heights from geostationary satellite pairs. Synchronization of observations between observing systems is not required. NASA and NOAA stereo-winds codes have implemented this method and we have processed large datasets from GOES-16, -17, and Himawari-8. Our retrievals are validated against rawinsonde observations and demonstrate the potential to improve forecast skill. Stereo winds also offer an important mitigation for the loop heat pipe anomaly on GOES-17 during times when warm focal plane temperatures cause infra-red channels that are needed for operational height assignments to fail. We also examine several application areas, including deep convection in tropical cyclones, planetary boundary layer dynamics, and fire smoke plumes, where stereo methods provide insights into atmospheric processes. The stereo method is broadly applicable across the geostationary ring where systems offering similar Image Navigation and Registration (INR) performance as GOES-R are deployed.

Atmospheric Motion Vectors from Model Simulations. Part II: Interpretation as Spatial and Vertical Averages of Wind and Role of Clouds

2014 ◽  
Vol 53 (1) ◽  
pp. 65-82 ◽  
Author(s):  
Angeles Hernandez-Carrascal ◽  
Niels Bormann
Keyword(s):  
Weather Prediction ◽  
Cloud Layer ◽  
Cloud Base ◽  
Random Errors ◽  
Motion Vectors ◽  
Single Level ◽  
Model Cloud ◽  
Atmospheric Motion ◽  
Nwp Model ◽  
Atmospheric Motion Vectors

AbstractThis is the second part of a two-part paper whose main objective is to improve the characterization of atmospheric motion vectors (AMVs) and their errors to guide developments in the use of AMVs in numerical weather prediction (NWP). AMVs tend to exhibit considerable systematic and random errors. These errors can arise in the AMV derivation or the interpretation of AMVs as single-level point estimates of wind. An important difficulty in the study of AMV errors is the scarcity of collocated observations of clouds and wind. The study uses instead a simulation framework: geostationary imagery for Meteorological Satellite-8 (Meteosat-8) is generated from a high-resolution simulation with the Weather Research and Forecasting regional model, and AMVs are derived from sequences of these simulated images. The NWP model provides the “truth” with a sophisticated description of the atmosphere. This second part focuses on alternative interpretations of AMVs. The key results are 1) that interpreting the AMVs as vertical and horizontal averages of wind can give some benefits over the traditional single-level interpretation (improvements in RMSVD of 5% for high-level AMVs and 20% for low-level AMVs) and 2) that there is evidence that AMVs are more representative of either a wind average over the model cloud layer or wind at a representative level within the cloud layer than of wind at the model cloud top or cloud base.

scholarly journals Tropical Cyclone Forecasts Impact Assessment from the Assimilation of Hourly Visible, Shortwave, and Clear-Air Water Vapor Atmospheric Motion Vectors in HWRF

2019 ◽  
Vol 34 (1) ◽  
pp. 177-198 ◽  
Author(s):  
Agnes H. N. Lim ◽  
James A. Jung ◽  
Sharon E. Nebuda ◽  
Jaime M. Daniels ◽  
Wayne Bresky ◽  
...  
Keyword(s):  
Water Vapor ◽  
Tropical Cyclone ◽  
Tropical Cyclones ◽  
Positive Impact ◽  
Data Types ◽  
Motion Vectors ◽  
Atmospheric Motion ◽  
Shortwave Infrared ◽  
Atmospheric Motion Vectors ◽  
The Impact

Abstract The assimilation of atmospheric motion vectors (AMVs) provides important wind information to conventional data-lacking oceanic regions, where tropical cyclones spend most of their lifetimes. Three new AMV types, shortwave infrared (SWIR), clear-air water vapor (CAWV), and visible (VIS), are produced hourly by NOAA/NESDIS and are assimilated in operational NWP systems. The new AMV data types are added to the hourly infrared (IR) and cloud-top water vapor (CTWV) AMV data in the 2016 operational version of the HWRF Model. In this study, we update existing quality control (QC) procedures and add new procedures specific to tropical cyclone assimilation. We assess the impact of the three new AMV types on tropical cyclone forecasts by conducting assimilation experiments for 25 Atlantic tropical cyclones from the 2015 and 2016 hurricane seasons. Forecasts are analyzed by considering all tropical cyclones as a group and classifying them into strong/weak storm vortices based on their initial model intensity. Metrics such as track error, intensity error, minimum central pressure error, and storm size are used to assess the data impact from the addition of the three new AMV types. Positive impact is obtained for these metrics, indicating that assimilating SWIR-, CAWV-, and VIS-type AMVs are beneficial for tropical cyclone forecasting. Given the results presented here, the new AMV types were accepted into NOAA/NCEP’s operational HWRF for the 2017 hurricane season.

scholarly journals Himawari-8 Atmospheric Motion Vectors - Operational Generation and Assimilation

2017 ◽  
Vol 67 (1) ◽  
pp. 12
Author(s):  
John Le Marshall ◽  
David Howard ◽  
Yi Xiao ◽  
Jamie Daniels ◽  
Steve Wanzong ◽  
...  
Keyword(s):  
Weather Prediction ◽  
Forecast Model ◽  
High Temporal Resolution ◽  
Motion Vectors ◽  
Atmospheric Motion ◽  
Nwp Model ◽  
Effective Use ◽  
New Generation ◽  
Atmospheric Motion Vectors ◽  
Resolution Data

In October 2014 the Japanese Meteorological Agency (JMA) launched the new generation geostationary satellite Himawari-8. This satellite provides ten minute imagery in sixteen wavebands over the Asian and Australasian region. The imagery has been navigated, calibrated and subsequently used in the Bureau of Meteorology (BoM) to generate Atmospheric Motion Vectors (AMVs) over the full earth disk viewed from the satellite every ten minutes. Each vector has been error characterised and assigned an expected error. In preparation for the operational assimilation of the ten minute data, these high temporal and spatial resolution data were used with the BoM operational database to provide forecasts from the next generation operational forecast model ACCESS APS2 using 4D Var. Results from these tests indicate these locally generated Himawari-8 ten minute AMVs are of high density and quality and have the potential to improve numerical weather prediction (NWP) model initialisation and forecasts. The forecasts undertaken include cases associated with extreme weather. The results also provided the appropriate times, data selection and application methods for the effective use of these high temporal resolution data. As a result of these studies these wind data were approved for inclusion in the BoMs operational database and are used in operational forecasting.

scholarly journals Effect of Enhanced Satellite-Derived Atmospheric Motion Vectors on Numerical Weather Prediction in East Asia Using an Adjoint-Based Observation Impact Method

2017 ◽  
Vol 32 (2) ◽  
pp. 579-594 ◽  
Author(s):  
Myunghwan Kim ◽  
Hyun Mee Kim ◽  
JinWoong Kim ◽  
Sung-Min Kim ◽  
Christopher Velden ◽  
...  
Keyword(s):  
Numerical Weather Prediction ◽  
Forecast Error ◽  
Weather Prediction ◽  
Motion Vectors ◽  
Numerical Weather ◽  
Atmospheric Motion ◽  
Observation Impact ◽  
Total Observation ◽  
Atmospheric Motion Vectors ◽  
The Impact

Abstract When producing forecasts by integrating a numerical weather prediction model from an analysis, not all observations assimilated into the analysis improve the forecast. Therefore, the impact of particular observations on the forecast needs to be evaluated quantitatively to provide relevant information about the impact of the observing system. One way to assess the observation impact is to use an adjoint-based method that estimates the impact of each assimilated observation on reducing the error of the forecast. In this study, the Weather Research and Forecasting Model and its adjoint are used to evaluate the impact of several types of observations, including enhanced satellite-derived atmospheric motion vectors (AMVs) that were made available during observation campaigns for two typhoons: Sinlaku and Jangmi, which both formed in the western North Pacific during September 2008. Without the assimilation of enhanced AMV data, radiosonde observations and satellite radiances show the highest total observation impact on forecasts. When enhanced AMVs are included in the assimilation, the observation impact of AMVs is increased and the impact of radiances is decreased. The highest ratio of beneficial observations comes from GPS Precipitable Water (GPSPW) without the assimilation of enhanced AMVs. Most observations express a ratio of approximately 60%. Enhanced AMVs improve forecast fields when tracking the typhoon centers of Sinlaku and Jangmi. Both the model background and the analysis are improved by the continuous cycling of enhanced AMVs, with a greater reduction in forecast error along the background trajectory than the analysis trajectory. Thus, while the analysis–forecast system is improved by assimilating these observations, the total observation impact is smaller as a result of the improvement.

scholarly journals GEO–GEO Stereo-Tracking of Atmospheric Motion Vectors (AMVs) from the Geostationary Ring

2020 ◽  
Vol 12 (22) ◽  
pp. 3779
Author(s):  
James L. Carr ◽  
Dong L. Wu ◽  
Jaime Daniels ◽  
Mariel D. Friberg ◽  
Wayne Bresky ◽  
...  
Keyword(s):  
Deep Convection ◽  
Motion Vectors ◽  
Fire Smoke ◽  
Atmospheric Motion ◽  
Image Navigation ◽  
Boundary Layer Dynamics ◽  
Vantage Points ◽  
Height Assignment ◽  
Atmospheric Motion Vectors ◽  
Geostationary Ring

Height assignment is an important problem for satellite measurements of atmospheric motion vectors (AMVs) that are interpreted as winds by forecast and assimilation systems. Stereo methods assign heights to AMVs from the parallax observed between observations from different vantage points in orbit while tracking cloud or moisture features. In this paper, we fully develop the stereo method to jointly retrieve wind vectors with their geometric heights from geostationary satellite pairs. Synchronization of observations between observing systems is not required. NASA and NOAA stereo-winds codes have implemented this method and we processed large datasets from GOES-16, -17, and Himawari-8. Our retrievals are validated against rawinsonde observations and demonstrate the potential to improve the forecast skill. Stereo winds also offer an important mitigation for the loop heat pipe anomaly on GOES-17 during times when warm focal plane temperatures cause infrared channels that are needed for operational height assignments to fail. We also examine several application areas, including deep convection in tropical cyclones, planetary boundary layer dynamics, and fire smoke plumes, where stereo methods provide insights into atmospheric processes. The stereo method is broadly applicable across the geostationary ring where systems offering similar image navigation and registration (INR) performance as GOES-R are deployed.

scholarly journals Assimilation of High-Resolution Satellite-Derived Atmospheric Motion Vectors: Impact on HWRF Forecasts of Tropical Cyclone Track and Intensity

2017 ◽  
Vol 145 (3) ◽  
pp. 1107-1125 ◽  
Author(s):  
Christopher Velden ◽  
William E. Lewis ◽  
Wayne Bresky ◽  
David Stettner ◽  
Jaime Daniels ◽  
...  
Keyword(s):  
Quality Control ◽  
High Resolution ◽  
Tropical Cyclone ◽  
Cyclone Track ◽  
Tropical Cyclone Track ◽  
Motion Vectors ◽  
Atmospheric Motion ◽  
Atmospheric Motion Vectors ◽  
The Impact ◽  
Atlantic Tropical Cyclone

It is well known that global numerical model analyses and forecasts benefit from the routine assimilation of atmospheric motion vectors (AMVs) derived from meteorological satellites. Recent studies have also shown that the assimilation of enhanced (spatial and temporal) AMVs can benefit research-mode regional model forecasts of tropical cyclone track and intensity. In this study, the impact of direct assimilation of enhanced (higher resolution) AMV datasets in the NCEP operational Hurricane Weather Research and Forecasting Model (HWRF) system is investigated. Forecasts of Atlantic tropical cyclone track and intensity are examined for impact by inclusion of enhanced AMVs via direct data assimilation. Experiments are conducted for AMVs derived using two methodologies (“HERITAGE” and “GOES-R”), and also for varying levels of quality control in order to assess and inform the optimization of the AMV assimilation process. Results are presented for three selected Atlantic tropical cyclone events and compared to Control forecasts without the enhanced AMVs as well as the corresponding operational HWRF forecasts. The findings indicate that the direct assimilation of high-resolution AMVs has an overall modest positive impact on HWRF forecasts, but the impact magnitudes are dependent on the 1) availability of rapid scan imagery used to produce the AMVs, 2) AMV derivation approach, 3) level of quality control employed in the assimilation, and 4) vortex initialization procedure (including the degree to which unbalanced states are allowed to enter the model analyses).

The Impact of Satellite-Derived Atmospheric Motion Vectors on Mesoscale Forecasts over Hawaii*

2006 ◽  
Vol 134 (7) ◽  
pp. 2009-2020 ◽  
Author(s):  
T. Cherubini ◽  
S. Businger ◽  
C. Velden ◽  
R. Ogasawara
Keyword(s):  
Mesoscale Model ◽  
Initial Conditions ◽  
Positive Impact ◽  
Local Analysis ◽  
Central Pacific ◽  
Motion Vectors ◽  
Atmospheric Motion ◽  
The University ◽  
Atmospheric Motion Vectors ◽  
The Impact

Abstract Tropospheric motions can be inferred from geostationary satellites by tracking clouds and water vapor in sequential imagery. These atmospheric motion vectors (AMV) have been operationally assimilated into global models for the past three decades, with positive forecast impacts. This paper presents results from a study to assess the impact of AMV derived from Geostationary Operational Environmental Satellite (GOES) imagery on mesoscale forecasts over the conventional data-poor central North Pacific region. These AMV are derived using the latest automated processing methodologies by the University of Wisconsin—Cooperative Institute for Meteorological Satellite Studies (CIMSS). For a test case, a poorly forecast subtropical cyclone (kona low) that occurred over Hawaii on 23–27 February 1997 was chosen. The Local Analysis and Prediction System (LAPS) was used to assimilate GOES-9 AMV data and to produce fifth-generation Pennsylvania State University–NCAR Mesoscale Model (MM5) initial conditions. The satellite wind assimilation is carried out on the 27-km-resolution domain covering the central Pacific area. The MM5 was run with three two-way nested domains (27, 9, and 3 km), with the innermost domain moving with the kona low. The AMV data are found to influence the cyclone’s development, improving the prediction of the cyclone’s central pressure and the track of the low’s center. Since September 2003, GOES-10 AMV data have been routinely accessed from CIMSS in real time and assimilated into the University of Hawaii (UH) LAPS, providing high-resolution initial conditions for twice-daily runs of MM5 at the Mauna Kea Weather Center collocated at the UH. It is found that the direct assimilation of AMV data into LAPS has a positive impact on the forecast accuracy of the UH LAPS/MM5 operational forecasting system when validated with observations in Hawaii. The implications of the results are discussed.

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