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.

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 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.

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

2014 ◽  
Vol 120 (3-4) ◽  
pp. 587-599 ◽  
Author(s):  
Inderpreet Kaur ◽  
Prashant Kumar ◽  
S. K. Deb ◽  
C. M. Kishtawal ◽  
P. K. Pal ◽  
...  
Keyword(s):  
Summer Monsoon ◽  
Mesoscale Model ◽  
Model Forecast ◽  
Motion Vectors ◽  
Atmospheric Motion ◽  
Atmospheric Motion Vectors

scholarly journals Development of a hybrid variational-ensemble data assimilation technique for observed lightning tested in a mesoscale model

2014 ◽  
Vol 21 (5) ◽  
pp. 1027-1041 ◽  
Author(s):  
K. Apodaca ◽  
M. Zupanski ◽  
M. DeMaria ◽  
J. A. Knaff ◽  
L. D. Grasso
Keyword(s):  
Data Assimilation ◽  
Prediction Models ◽  
Mesoscale Model ◽  
Initial Conditions ◽  
Positive Impact ◽  
Weather Prediction ◽  
New Information ◽  
Forecasting System ◽  
Maximum Likelihood Ensemble Filter ◽  
The Impact

Abstract. Lightning measurements from the Geostationary Lightning Mapper (GLM) that will be aboard the Geostationary Operational Environmental Satellite – R Series will bring new information that can have the potential for improving the initialization of numerical weather prediction models by assisting in the detection of clouds and convection through data assimilation. In this study we focus on investigating the utility of lightning observations in mesoscale and regional applications suitable for current operational environments, in which convection cannot be explicitly resolved. Therefore, we examine the impact of lightning observations on storm environment. Preliminary steps in developing a lightning data assimilation capability suitable for mesoscale modeling are presented in this paper. World Wide Lightning Location Network (WWLLN) data was utilized as a proxy for GLM measurements and was assimilated with the Maximum Likelihood Ensemble Filter, interfaced with the Nonhydrostatic Mesoscale Model core of the Weather Research and Forecasting system (WRF-NMM). In order to test this methodology, regional data assimilation experiments were conducted. Results indicate that lightning data assimilation had a positive impact on the following: information content, influencing several dynamical variables in the model (e.g., moisture, temperature, and winds), and improving initial conditions during several data assimilation cycles. However, the 6 h forecast after the assimilation did not show a clear improvement in terms of root mean square (RMS) errors.

scholarly journals Hybrid variational-ensemble assimilation of lightning observations in a mesoscale model

2014 ◽  
Vol 1 (1) ◽  
pp. 917-952
Author(s):  
K. Apodaca ◽  
M. Zupanski ◽  
M. DeMaria ◽  
J. A. Knaff ◽  
L. D. Grasso
Keyword(s):  
Data Assimilation ◽  
Prediction Models ◽  
Mesoscale Model ◽  
Initial Conditions ◽  
Positive Impact ◽  
Weather Prediction ◽  
New Information ◽  
Forecasting System ◽  
Maximum Likelihood Ensemble Filter ◽  
The Impact

Abstract. Lightning measurements from the Geostationary Lightning Mapper (GLM) that will be aboard the Goestationary Operational Environmental Satellite – R Series will bring new information that can have the potential for improving the initialization of numerical weather prediction models by assisting in the detection of clouds and convection through data assimilation. In this study we focus on investigating the utility of lightning observations in mesoscale and regional applications suitable for current operational environments, in which convection cannot be explicitly resolved. Therefore, we examine the impact of lightning observations on storm environment. Preliminary steps in developing a lightning data assimilation capability suitable for mesoscale modeling are presented in this paper. World Wide Lightning Location Network (WWLLN) data was utilized as a proxy for GLM measurements and was assimilated with the Maximum Likelihood Ensemble Filter, interfaced with the Nonhydrostatic Mesoscale Model core of the Weather Research and Forecasting system (WRF-NMM). In order to test this methodology, regional data assimilation experiments were conducted. Results indicate that lightning data assimilation had a positive impact on the following: information content, influencing several dynamical variables in the model (e.g., moisture, temperature, and winds), improving initial conditions, and partially improving WRF-NMM forecasts during several data assimilation cycles.

scholarly journals Height Correction of Atmospheric Motion Vectors Using Satellite Lidar Observations from CALIPSO

2014 ◽  
Vol 53 (7) ◽  
pp. 1809-1819 ◽  
Author(s):  
Kathrin Folger ◽  
Martin Weissmann
Keyword(s):  
Prediction Models ◽  
Initial Conditions ◽  
Weather Prediction ◽  
Current Data ◽  
Motion Vectors ◽  
Deep Layers ◽  
Atmospheric Motion ◽  
Height Assignment ◽  
Atmospheric Motion Vectors ◽  
Lidar Observations

AbstractAtmospheric motion vectors (AMVs) provide valuable wind information for the initial conditions of numerical weather prediction models, but height-assignment issues and horizontal error correlations require a rigid thinning of the available AMVs in current data assimilation systems. The aim of this study is to investigate the feasibility of correcting the pressure heights of operational AMVs from the geostationary satellites Meteosat-9 and Meteosat-10 with cloud-top heights derived from lidar observations by the polar-orbiting Cloud–Aerosol Lidar and Infrared Pathfinder Satellite Observations (CALIPSO) satellite. The study shows that the wind error of AMVs above 700 hPa is reduced by 12%–17% when AMV winds are assigned to 120-hPa-deep layers below the lidar cloud tops. This result demonstrates the potential of lidar cloud observations for the improvement of the AMV height assignment. In addition, the lidar correction reduces the “slow” bias of current upper-level AMVs and is expected to reduce the horizontal correlation of AMV errors.

scholarly journals Demonstration with Special TCI-15 Datasets of Potential Impacts of New-Generation Satellite Atmospheric Motion Vectors on Navy Regional and Global Models

2018 ◽  
Vol 33 (6) ◽  
pp. 1617-1637 ◽  
Author(s):  
Russell L. Elsberry ◽  
Eric A. Hendricks ◽  
Christopher S. Velden ◽  
Michael M. Bell ◽  
Melinda Peng ◽  
...  
Keyword(s):  
Initial Conditions ◽  
Three Dimensional ◽  
High Definition ◽  
Rapid Decay ◽  
Motion Vectors ◽  
Constant Intensity ◽  
Atmospheric Motion ◽  
Wind Shears ◽  
New Generation ◽  
Atmospheric Motion Vectors

Abstract A dynamic initialization assimilation scheme is demonstrated utilizing rapid-scan atmospheric motion vectors (AMVs) at 15-min intervals to simulate the real-time capability that now exists from the new generation of geostationary meteorological satellites. The impacts of these AMVs are validated with special Tropical Cyclone Intensity Experiment (TCI-15) datasets during 1200–1800 UTC 4 October leading up to a NASA WB-57 eyewall crossing of Hurricane Joaquin. Incorporating the AMV fields in the Spline Analysis at Mesoscale Utilizing Radar and Aircraft Instrumentation (SAMURAI) COAMPS Dynamic Initialization (SCDI) means there are 30 and 90 time steps on the 15- and 5-km grids, respectively, during which the mass fields are adjusted to these AMV-based wind increments during each 15-min assimilation period. The SCDI analysis of the three-dimensional vortex structure of Joaquin at 1800 UTC 4 October closely replicates the vortex tilt analyzed from the High-Definition Sounding System (HDSS) dropwindsondes. Vertical wind shears based on the AMVs at 15-min intervals are well correlated with the extreme rapid decay, an interruption of that rapid decay, and the subsequent period of constant intensity of Joaquin. Utilizing the SCDI analysis as the initial conditions for two versions of the COAMPS-TC model results in an accurate 72-h prediction of the interruption of the rapid decay and the period of constant intensity. Upscaling a similar SCDI analysis based on the 15-min interval AMVs provides a more realistic intensity and structure of Tropical Storm Joaquin for the initial conditions of the Navy Global Environmental Model (NAVGEM) than the synthetic TC vortex used operationally. This demonstration for a single 6-h period of AMVs indicates the potential for substantial impacts when an end-to-end cycling version is developed.

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 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 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).

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