Effect of Assimilating Himawari-8 Atmospheric Motion Vectors on Forecast Errors over East Asia

2018 ◽  
Vol 35 (9) ◽  
pp. 1737-1752 ◽  
Author(s):  
Dae-Hui Kim ◽  
Hyun Mee Kim
Keyword(s):  
East Asia ◽  
Geopotential Height ◽  
Forecast Error ◽  
Experimental Period ◽  
Energy Norm ◽  
Observation System ◽  
Forecast Errors ◽  
Motion Vectors ◽  
Atmospheric Motion ◽  
Atmospheric Motion Vectors

AbstractIn this study, the effect of assimilating Himawari-8 (HIMA-8) atmospheric motion vectors (AMVs) on forecast errors in East Asia is evaluated using observation system experiments based on the Weather Research and Forecasting Model and three-dimensional variational data assimilation system. The experimental period is from 1 August to 30 September 2015, during which both HIMA-8 and Multifunctional Transport Satellite-2 (MTSAT-2) AMVs exist. The energy-norm forecast error based on the analysis of each experiment as reference was reduced more by replacing MTSAT-2 AMVs with HIMA-8 AMVs than by adding HIMA-8 AMVs to the MTSAT-2 AMVs. When the HIMA-8 AMVs replaced or were added to MTSAT-2 AMVs, the observation impact was reduced, which implies the analysis–forecast system was improved by assimilating HIMA-8 AMVs. The root-mean-square error (RMSE) of the 500-hPa geopotential height forecasts based on the analysis of each experiment decreases more effectively when the region lacking in upper-air wind observations is reduced by assimilating both MTSAT-2 and HIMA-8 AMVs. When the upper-air radiosonde (SOUND) observations are used as reference, assimilating more HIMA-8 AMVs decreases the forecast error. Based on various measures, the assimilation of HIMA-8 AMVs has a positive effect on the reduction of forecast errors. The effects on the energy-norm forecast error and the RMSE based on SOUND observations are greater when HIMA-8 AMVs replaced MTSAT-2 AMVs. However, the effects on the RMSE of the 500-hPa geopotential height forecasts are greater when both HIMA-8 and MTSAT-2 AMVs were assimilated, which implies potential benefits of assimilating AMVs from several satellites for forecasts over East Asia depending on the choice of measurement.

scholarly journals Strategies for Assimilating High-Density Atmospheric Motion Vectors into a Regional Tropical Cyclone Forecast Model (HWRF)

Atmosphere ◽  
2020 ◽  
Vol 11 (6) ◽  
pp. 673 ◽  
Author(s):  
William E. Lewis ◽  
Christopher S. Velden ◽  
David Stettner
Keyword(s):  
Forecast Error ◽  
Forecast Model ◽  
High Density ◽  
Track Forecast ◽  
Forecast Errors ◽  
Spatiotemporal Resolution ◽  
Motion Vectors ◽  
Weather Events ◽  
Atmospheric Motion ◽  
Atmospheric Motion Vectors

In recent years, atmospheric numerical modeling frameworks and satellite observing systems have both undergone significant advances. While these developments offer considerable potential for improving forecasts of high-impact weather events such as tropical cyclones (TC), much work remains to be done regarding the targeted processing and optimal use of observations now becoming available with high spatiotemporal resolution. Using the 2019 version of NCEP’s HWRF model, we explore several different strategies for the assimilation of TC-scale, high-density atmospheric motion vectors (AMVs) derived from the new-generation GOES-R series of geostationary satellites. Using 2017’s Atlantic Hurricane Irma as a case study, we examine the HWRF forecast impacts of observation pre-processing, including thinning and adjustments to observation errors. It is demonstrated that enhanced vortex-scale GOES-16 AMVs contribute to notable improvements in HWRF track forecast error compared to a baseline control experiment that does not incorporate the high-density AMVs. Impacts on TC intensity and structure (i.e., wind radii) forecast errors are less robust, but results from the optimization experiments suggest that further work (both with regard to data assimilation strategies and advancements in the methods themselves) should lead to improvements in these forecast variables as well.

scholarly journals Forecast Sensitivity Observation Impact in the 4DVAR and Hybrid-4DVAR Data Assimilation Systems

2019 ◽  
Vol 36 (8) ◽  
pp. 1563-1575 ◽  
Author(s):  
Sung-Min Kim ◽  
Hyun Mee Kim
Keyword(s):  
Data Assimilation ◽  
East Asia ◽  
Forecast Error ◽  
Integration Time ◽  
Ensemble Forecasts ◽  
Background Error ◽  
Error Covariance ◽  
Atmospheric Motion ◽  
Observation Impact ◽  
Atmospheric Motion Vectors

AbstractIn this study, the observation impacts on 24-h forecast error reduction (FER), based on the adjoint method in the four-dimensional variational (4DVAR) data assimilation (DA) and hybrid-4DVAR DA systems coupled with the Unified Model, were evaluated from 0000 UTC 5 August to 1800 UTC 26 August 2014. The nonlinear FER in hybrid-4DVAR was 12.2% greater than that in 4DVAR due to the use of flow-dependent background error covariance (BEC), which was a weighted combination of the static BEC and the ensemble BEC based on ensemble forecasts. In hybrid-4DVAR, the observation impacts (i.e., the approximated nonlinear FER) for most observation types increase compared to those in 4DVAR. The increased observation impact from using hybrid-4DVAR instead of 4DVAR changes depending on the analysis time and regions. To calculate the ensemble BEC in hybrid-4DVAR, analyses at 0600 and 1800 UTC (0000 and 1200 UTC) used 3-h (9-h) ensemble forecasts. Greater observation impact was obtained when 3-h ensemble forecasts were used for the ensemble BEC at 0600 and 1800 UTC, than with 9-h ensemble forecasts at 0000 and 1200 UTC. Different from other observations, the atmospheric motion vectors (AMVs) deduced from geostationary satellite are more frequently observed in the same area. When the ensemble forecasts with longer integration times were used for the ensemble BEC in hybrid-4DVAR, the observation impact of the AMVs decreased the most in East Asia. This implies that the observation impact of AMVs in East Asia shows the highest sensitivity to the integration time of the ensemble members used for deducing the flow-dependent BEC in hybrid-4DVAR.

Observation System Experiments with the Hourly Updating Rapid Refresh Model Using GSI Hybrid Ensemble–Variational Data Assimilation

2017 ◽  
Vol 145 (8) ◽  
pp. 2897-2918 ◽  
Author(s):  
Eric P. James ◽  
Stanley G. Benjamin
Keyword(s):  
Forecast Error ◽  
Weather Prediction ◽  
Precipitable Water ◽  
Separate Experiment ◽  
Observation System ◽  
The North ◽  
Atmospheric Motion ◽  
Atmospheric Motion Vectors ◽  
Initial Results ◽  
Surface Observations

A set of observation system experiments (OSEs) over three seasons using the hourly updated Rapid Refresh (RAP) numerical weather prediction (NWP) assimilation–forecast system identifies the importance of the various components of the North American observing system for 3–12-h RAP forecasts. Aircraft observations emerge as the strongest-impact observation type for wind, relative humidity (RH), and temperature forecasts, permitting a 15%–30% reduction in 6-h forecast error in the troposphere and lower stratosphere. Major positive impacts are also seen from rawinsondes, GOES satellite cloud observations, and surface observations, with lesser but still significant impacts from GPS precipitable water (PW) observations, satellite atmospheric motion vectors (AMVs), and radar reflectivity observations. A separate experiment revealed that the aircraft-related RH forecast improvement was augmented by 50% due specifically to the addition of aircraft moisture observations. Additionally, observations from en route aircraft and those from ascending or descending aircraft contribute approximately equally to the overall forecast skill, with the strongest impacts in the respective layers of the observations. Initial results from these OSEs supported implementation of an improved assimilation configuration of boundary layer pseudoinnovations from surface observations, as well as allowing the assimilation of satellite AMVs over land. The breadth of these experiments over the three seasons suggests that observation impact results are applicable to general forecasting skill, not just classes of phenomena during limited time periods.

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 The Sentinel-3 SLSTR Atmospheric Motion Vectors Product at EUMETSAT

2021 ◽  
Vol 13 (9) ◽  
pp. 1702
Author(s):  
Kévin Barbieux ◽  
Olivier Hautecoeur ◽  
Maurizio De Bartolomei ◽  
Manuel Carranza ◽  
Régis Borde
Keyword(s):  
Land Surface ◽  
Weather Prediction ◽  
Forecast Model ◽  
Low Earth Orbit ◽  
Reference Image ◽  
Motion Vectors ◽  
Infrared Imager ◽  
Atmospheric Motion ◽  
Atmospheric Motion Vectors ◽  
Leo Satellite

Atmospheric Motion Vectors (AMVs) are an important input to many Numerical Weather Prediction (NWP) models. EUMETSAT derives AMVs from several of its orbiting satellites, including the geostationary satellites (Meteosat), and its Low-Earth Orbit (LEO) satellites. The algorithm extracting the AMVs uses pairs or triplets of images, and tracks the motion of clouds or water vapour features from one image to another. Currently, EUMETSAT LEO satellite AMVs are retrieved from georeferenced images from the Advanced Very-High-Resolution Radiometer (AVHRR) on board the Metop satellites. EUMETSAT is currently preparing the operational release of an AMV product from the Sea and Land Surface Temperature Radiometer (SLSTR) on board the Sentinel-3 satellites. The main innovation in the processing, compared with AVHRR AMVs, lies in the co-registration of pairs of images: the images are first projected on an equal-area grid, before applying the AMV extraction algorithm. This approach has multiple advantages. First, individual pixels represent areas of equal sizes, which is crucial to ensure that the tracking is consistent throughout the processed image, and from one image to another. Second, this allows features that would otherwise leave the frame of the reference image to be tracked, thereby allowing more AMVs to be derived. Third, the same framework could be used for every LEO satellite, allowing an overall consistency of EUMETSAT AMV products. In this work, we present the results of this method for SLSTR by comparing the AMVs to the forecast model. We validate our results against AMVs currently derived from AVHRR and the Spinning Enhanced Visible and InfraRed Imager (SEVIRI). The release of the operational SLSTR AMV product is expected in 2022.

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.

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 Enhanced Vortex-Scale Atmospheric Motion Vectors for Hurricane Applications

2019 ◽  
Vol 11 (17) ◽  
pp. 1981 ◽  
Author(s):  
David Stettner ◽  
Christopher Velden ◽  
Robert Rabin ◽  
Steve Wanzong ◽  
Jaime Daniels ◽  
...  
Keyword(s):  
Weather Prediction ◽  
Global Scale ◽  
Motion Vectors ◽  
Weather Events ◽  
Convective Scale ◽  
Satellite Sensors ◽  
Atmospheric Motion ◽  
Scanning Strategies ◽  
New Generation ◽  
Atmospheric Motion Vectors

Atmospheric motion vectors (AMVs) derived from geostationary meteorological satellites have long stood as an important observational contributor to analyses of global-scale tropospheric wind patterns. This paradigm is evolving as numerical weather prediction (NWP) models and associated data assimilation systems are at the point of trying to better resolve finer scales. Understanding the physical processes that govern convectively-driven weather systems is usually hindered by a lack of observations on the scales necessary to adequately describe these events. Fortunately, satellite sensors and associated scanning strategies have improved and are now able to resolve convective-scale flow fields. Coupled with the increased availability of computing capacity and more sophisticated algorithms to track cloud motions, we are now poised to investigate the development and application of AMVs to convective-scale weather events. Our study explores this frontier using new-generation GOES-R Series imagery with a focus on hurricane applications. A proposed procedure for processing enhanced AMV datasets derived from multispectral geostationary satellite imagery for hurricane-scale analyses is described. We focus on the use of the recently available GOES-16 mesoscale domain sector rapid-scan (1-min) imagery, and emerging methods to optimally extract wind estimates (atmospheric motion vectors (AMVs)) from close-in-time sequences. It is shown that AMV datasets can be generated on spatiotemporal scales not only useful for global applications, but for mesoscale applications such as hurricanes as well.

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