The impact of GMS (Geostationary Meteorological Satellite) data in the GDAPS (Global Data Assimilation and Prediction System)

Author(s):  
Sang-Won Joo ◽  
Nam-Ouk Kim
1990 ◽  
Vol 118 (12) ◽  
pp. 2513-2542 ◽  
Author(s):  
Ross N. Hoffman ◽  
Christopher Grassotti ◽  
Ronald G. Isaacs ◽  
Jean-Francois Louis ◽  
Thomas Nehrkorn ◽  
...  

2006 ◽  
Vol 21 (4) ◽  
pp. 663-669 ◽  
Author(s):  
Dongliang Wang ◽  
Xudong Liang ◽  
Yihong Duan ◽  
Johnny C. L. Chan

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.


2019 ◽  
Vol 146 (726) ◽  
pp. 401-414 ◽  
Author(s):  
Robert R. King ◽  
Daniel J. Lea ◽  
Matthew J. Martin ◽  
Isabelle Mirouze ◽  
Julian Heming

MAUSAM ◽  
2021 ◽  
Vol 50 (2) ◽  
pp. 177-180
Author(s):  
C. M. MUKAMMEL WARID ◽  
Md. REZAUR RAHMAN ◽  
Md. NAZRUL ISLAM

Multi-cell and single-cell clouds were analysed using Geostationary Meteorological Satellite (GMS-5) data on 6 and 13 August 1997 in and around Bangladesh. The multi-cell cloud moved NE with a speed of about 6 m/s and lasted approximately 21 hours. The single-cell cloud moved SE with a speed of about 13 m/s and lasted approximately 12 hours. Clouds move faster on oceans than on land. At the mature stage of the cloud, convective component was 40% and the rest was stratiform. The precipitable portion of the cloud was 74% and the rest was non-precipitable which differs from the reported value.


2017 ◽  
Author(s):  
Simon Verrier ◽  
Pierre-Yves Le Traon ◽  
Elisabeth Remy

Abstract. A series of Observing System Simulation Experiments (OSSEs) is carried out with a global data assimilation system at 1/4° resolution using simulated data derived from a 1/12° resolution free run simulation. The objective is to quantify how well multiple altimeter missions and Argo profiling floats can constrain a global data assimilation system but also to better understand the sensitivity of results to data assimilation techniques used in Mercator Ocean operational systems. Impact of multiple altimeter data is clearly evidenced. Forecasts of sea level and ocean currents are significantly improved when moving from one altimeter to two altimeters. In high eddy energy regions, sea level and surface current forecast errors when assimilating one altimeter data set are respectively 20 % and 45 % of the error of the simulation without assimilation. Forecasts of sea level and ocean currents continue to be improved when moving from one altimeter to two altimeters with a relative error reduction of almost 30 %. The addition of a third altimeter still improves the forecasts even at this medium 1/4° resolution and brings an additional relative error reduction of about 10 %. The error level of the analysis with one altimeter is close to the forecast error level when two or three altimeter data sets are assimilated. Assimilating altimeter data also improves the representation of the 3D ocean fields. The addition of Argo has a major impact to improve temperature and demonstrates the essential role of Argo together with altimetry to constrain a global data assimilation system. Salinity fields are only marginally improved. Results derived from these OSSEs are consistent with those derived from experiments with real data (observing system evaluations/OSEs) but they allow a more detailed characterization of errors on analyses and forecasts. Both OSEs and OSSEs should be systematically used and intercompared to test data assimilation systems and quantify the impact of existing observing systems.


2016 ◽  
Vol 43 (11) ◽  
pp. 5886-5894 ◽  
Author(s):  
K. Yumimoto ◽  
T.M. Nagao ◽  
M. Kikuchi ◽  
T.T Sekiyama ◽  
H. Murakami ◽  
...  

2017 ◽  
Vol 32 (2) ◽  
pp. 595-608
Author(s):  
Tong Zhu ◽  
Sid Ahmed Boukabara ◽  
Kevin Garrett

Abstract The impacts of both satellite data assimilation (DA) and lateral boundary conditions (LBCs) on the Hurricane Weather Research and Forecasting (HWRF) Model forecasts of Hurricane Sandy 2012 were assessed. To investigate the impact of satellite DA, experiments were run with and without satellite data assimilated, as well as with all satellite data but excluding Geostationary Operational Environmental Satellite (GOES) Sounder data. To gauge the LBC impact, these experiments were also run with a variety of outer domain (D-1) sizes. The inclusion of satellite DA resulted in analysis fields that better characterized the tropical storm structures including the warm core anomaly and wavenumber-1 asymmetry near the eyewall, and also served to reduce the forecast track errors for Hurricane Sandy. The specific impact of assimilating the GOES Sounder data showed positive impacts on forecasts of the storm minimum sea level pressure. Increasing the D-1 size resulted in increases in the day 4/5 forecast track errors when verified against the best track and the Global Forecast System (GFS) forecast, which dominated any benefits from assimilating an increased volume of satellite observations due to the larger domain. It was found that the LBCs with realistic environmental flow information could provide better constraints on smaller domain forecasts. This study demonstrated that satellite DA can improve the analysis of a hurricane asymmetry, especially in a shear environment, and then lead to a better track forecast, and also emphasized the importance of the LBCs and the challenges associated with the evaluation of satellite data impacts on regional model prediction.


2016 ◽  
Vol 31 (1) ◽  
pp. 297-327 ◽  
Author(s):  
Thomas A. Jones ◽  
Kent Knopfmeier ◽  
Dustan Wheatley ◽  
Gerald Creager ◽  
Patrick Minnis ◽  
...  

Abstract This research represents the second part of a two-part series describing the development of a prototype ensemble data assimilation system for the Warn-on-Forecast (WoF) project known as the NSSL Experimental WoF System for ensembles (NEWS-e). Part I describes the NEWS-e design and results from radar reflectivity and radial velocity data assimilation for six severe weather events occurring during 2013 and 2014. Part II describes the impact of assimilating satellite liquid and ice water path (LWP and IWP, respectively) retrievals from the GOES Imager along with the radar observations. Assimilating LWP and IWP observations may improve thermodynamic conditions at the surface over the storm-scale domain through better analysis of cloud coverage in the model compared to radar-only experiments. These improvements sometimes corresponded to an improved analysis of supercell storms leading to better forecasts of low-level vorticity. This positive impact was most evident for events where convection is not ongoing at the beginning of the radar and satellite data assimilation period. For more complex cases containing significant amounts of ongoing convection, only assimilating clear-sky satellite retrievals in place of clear-air reflectivity resulted in spurious regions of light precipitation compared to the radar-only experiments. The analyzed tornadic storms in these experiments are sometimes too weak and quickly diminished in intensity in the forecasts. The lessons learned as part of these experiments should lead to improved iterations of the NEWS-e system, building on the modestly successful results described here.


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