scholarly journals The Impact of High-Resolution SALLJEX Data on Global NCEP Analyses

2007 ◽  
Vol 20 (23) ◽  
pp. 5765-5783 ◽  
Author(s):  
Dirceu L. Herdies ◽  
Vernon E. Kousky ◽  
Wesley Ebisuzaki
Keyword(s):  
Data Assimilation ◽  
Similar Data ◽  
Climate Data ◽  
Low Level ◽  
The Andes ◽  
Pilot Balloon ◽  
Low Level Jet ◽  
The Impact ◽  
Data Assimilation System ◽  
Assimilation System

Abstract A data assimilation study was performed to assess the impact of observations from the South American Low-Level Jet Experiment (SALLJEX) on analyses in the region east of the Andes Mountains from western Brazil to central Argentina. The Climate Data Assimilation Systems (CDAS)-1 and -2 and the Global Data Assimilation System (GDAS) were run with and without the additional SALLJEX rawinsondes and pilot balloon observations. The experiments for each data assimilation system revealed similar features, with a stronger low-level flow east of the Andes when SALLJEX data were included. GDAS had the strongest low-level jet (LLJ) when compared with observations. In the experiments that used additional rawinsonde and pilot balloon data, the LLJ was displaced westward in comparison to the analyses run without the SALLJEX data. The vertical structure of the meridional wind in the analyses was much closer to observed rawinsonde profiles in the experiments that included SALLJEX data than in the control experiments, and the results show that, although there are more pilot balloon observations than rawinsonde observations in the SALLJEX dataset, most of the improvements in the analyses can be obtained by only including rawinsonde observations. This was especially true for GDAS. The results of this study can serve as a benchmark for similar data impact studies using higher-resolution data assimilation systems.

Influence of the North American Monsoon Experiment (NAME) 2004 Enhanced Soundings on NCEP Operational Analyses

2007 ◽  
Vol 20 (9) ◽  
pp. 1821-1842 ◽  
Author(s):  
Kingtse C. Mo ◽  
Eric Rogers ◽  
Wesley Ebisuzaki ◽  
R. Wayne Higgins ◽  
J. Woollen ◽  
...  
Keyword(s):  
Data Assimilation ◽  
North American ◽  
Gulf Of California ◽  
North American Monsoon ◽  
Climate Data ◽  
Regional Data ◽  
Low Level ◽  
Low Level Jet ◽  
Data Assimilation System ◽  
Assimilation System

Abstract During the 2004 North American Monsoon Experiment (NAME) field campaign, an extensive set of enhanced atmospheric soundings was gathered over the southwest United States and Mexico. Most of these soundings were assimilated into the NCEP operational global and regional data assimilation systems in real time. This presents a unique opportunity to carry out a series of data assimilation experiments to examine their influence on the NCEP analyses and short-range forecasts. To quantify these impacts, several data-withholding experiments were carried out using the global Climate Data Assimilation System (CDAS), the Regional Climate Data Assimilation System (RCDAS), and the three-dimensional variational data assimilation (3DVAR) Eta Model Data Assimilation System (EDAS) for the NAME 2004 enhanced observation period (EOP). The impacts of soundings vary between the assimilation systems examined in this study. Overall, the influence of the enhanced soundings is concentrated over the core monsoon area. While differences at upper levels are small, the differences at lower levels are more substantial. The coarse-resolution CDAS does not properly resolve the Gulf of California (GoC), so the assimilation system is not able to exploit the additional soundings to improve characteristics of the Gulf of California low-level jet (GCLLJ) and the associated moisture transport in the GoC region. In contrast, the GCLLJ produced by RCDAS is conspicuously stronger than the observations, though the problem is somewhat alleviated with additional special NAME soundings. For EDAS, soundings improve the intensity and position of the Great Plains low-level jet (GPLLJ). The soundings in general improve the analyses over the areas where the assimilation system has the largest uncertainties and errors. However, the differences in regional analyses owing to the soundings are smaller than the differences between the two regional data assimilation systems.

Assessment of the Impact of Simulated Satellite Lidar Wind and Retrieved 183 GHz Water Vapor Observations on a Global Data Assimilation System

1990 ◽  
Vol 118 (12) ◽  
pp. 2513-2542 ◽  
Author(s):  
Ross N. Hoffman ◽  
Christopher Grassotti ◽  
Ronald G. Isaacs ◽  
Jean-Francois Louis ◽  
Thomas Nehrkorn ◽  
...  
Keyword(s):  
Water Vapor ◽  
Data Assimilation ◽  
Global Data Assimilation System ◽  
The Impact ◽  
Data Assimilation System ◽  
Global Data ◽  
Assimilation System

scholarly journals Impact of enriched analyses on regional numerical forecasts over southeastern South America during SALLJEX

2014 ◽  
Vol 29 (3) ◽  
pp. 315-330
Author(s):  
Yanina García Skabar ◽  
Matilde Nicolini
Keyword(s):  
Data Assimilation ◽  
South America ◽  
Atmospheric Modeling ◽  
South American ◽  
The South ◽  
Low Level ◽  
Positive Effects ◽  
Accumulated Rainfall ◽  
Low Level Jet ◽  
The Impact

During the warm season 2002-2003, the South American Low-Level Jet Experiment (SALLJEX) was carried out in southeastern South America. Taking advantage of the unique database collected in the region, a set of analyses is generated for the SALLJEX period assimilating all available data. The spatial and temporal resolution of this new set of analyses is higher than that of analyses available up to present for southeastern South America. The aim of this paper is to determine the impact of assimilating data into initial fields on mesoscale forecasts in the region, using the Brazilian Regional Atmospheric Modeling System (BRAMS) with particular emphasis on the South American Low-Level Jet (SALLJ) structure and on rainfall forecasts. For most variables, using analyses with data assimilated as initial fields has positive effects on short term forecast. Such effect is greater in wind variables, but not significant in forecasts longer than 24 hours. In particular, data assimilation does not improve forecasts of 24-hour accumulated rainfall, but it has slight positive effects on accumulated rainfall between 6 and 12 forecast hours. As the main focus is on the representation of the SALLJ, the effect of data assimilation in its forecast was explored. Results show that SALLJ is fairly predictable however assimilating additional observation data has small impact on the forecast of SALLJ timing and intensity. The strength of the SALLJ is underestimated independently of data assimilation. However, Root mean square error (RMSE) and BIAS values reveal the positive effect of data assimilation up to 18-hours forecasts with a greater impact near higher topography.

Evaluating the Impact of the Number of Satellite Altimeters Used in an Assimilative Ocean Prediction System

2010 ◽  
Vol 27 (3) ◽  
pp. 528-546 ◽  
Author(s):  
Robert W. Helber ◽  
Jay F. Shriver ◽  
Charlie N. Barron ◽  
Ole Martin Smedstad
Keyword(s):  
Data Assimilation ◽  
Statistical Error ◽  
Global Ocean ◽  
Height Anomaly ◽  
Naval Research ◽  
Thermocline Depth ◽  
Dynamic Height ◽  
The Impact ◽  
Data Assimilation System ◽  
Assimilation System

Abstract The impact of the number of satellite altimeters providing sea surface height anomaly (SSHA) information for a data assimilation system is evaluated using two comparison frameworks and two statistical methodologies. The Naval Research Laboratory (NRL) Layered Ocean Model (NLOM) dynamically interpolates satellite SSHA track data measured from space to produce high-resolution (eddy resolving) fields. The Modular Ocean Data Assimilation System (MODAS) uses the NLOM SSHA to produce synthetic three-dimensional fields of temperature and salinity over the global ocean. A series of case studies is defined where NLOM assimilates different combinations of data streams from zero to three altimeters. The resulting NLOM SSHA fields and the MODAS synthetic profiles are evaluated relative to independently observed ocean temperature and salinity profiles for the years 2001–03. The NLOM SSHA values are compared with the difference of the observed dynamic height from the climatological dynamic height. The synthetics are compared with observations using a measure of thermocline depth. Comparisons are done point for point and for 1° radius regions that are linearly fit over 2-month periods. To evaluate the impact of data outliers, statistical evaluations are done with traditional Gaussian statistics and also with robust nonparametric statistics. Significant error reduction is obtained, particularly in high SSHA variability regions, by including at least one altimeter. Given the limitation of these methods, the overall differences between one and three altimeters are significant only in bias. Data outliers increase Gaussian statistical error and error uncertainty compared to the same computations using nonparametric statistical methods.

scholarly journals CIRA/CSU Four-Dimensional Variational Data Assimilation System

2005 ◽  
Vol 133 (4) ◽  
pp. 829-843 ◽  
Author(s):  
Milija Zupanski ◽  
Dusanka Zupanski ◽  
Tomislava Vukicevic ◽  
Kenneth Eis ◽  
Thomas Vonder Haar
Keyword(s):  
Data Assimilation ◽  
Initial Conditions ◽  
Control Variable ◽  
Three Dimensional ◽  
Atmospheric Modeling ◽  
Variational Data Assimilation ◽  
Regional Atmospheric Modeling ◽  
The Impact ◽  
Data Assimilation System ◽  
Assimilation System

A new four-dimensional variational data assimilation (4DVAR) system is developed at the Cooperative Institute for Research in the Atmosphere (CIRA)/Colorado State University (CSU). The system is also called the Regional Atmospheric Modeling Data Assimilation System (RAMDAS). In its present form, the 4DVAR system is employing the CSU/Regional Atmospheric Modeling System (RAMS) nonhydrostatic primitive equation model. The Weather Research and Forecasting (WRF) observation operator is used to access the observations, adopted from the WRF three-dimensional variational data assimilation (3DVAR) algorithm. In addition to the initial conditions adjustment, the RAMDAS includes the adjustment of model error (bias) and lateral boundary conditions through an augmented control variable definition. Also, the control variable is defined in terms of the velocity potential and streamfunction instead of the horizontal winds. The RAMDAS is developed after the National Centers for Environmental Prediction (NCEP) Eta 4DVAR system, however with added improvements addressing its use in a research environment. Preliminary results with RAMDAS are presented, focusing on the minimization performance and the impact of vertical correlations in error covariance modeling. A three-dimensional formulation of the background error correlation is introduced and evaluated. The Hessian preconditioning is revisited, and an alternate algebraic formulation is presented. The results indicate a robust minimization performance.

scholarly journals The impact of the assimilation of Aquarius sea surface salinity data in the GEOS ocean data assimilation system

2014 ◽  
Vol 119 (10) ◽  
pp. 6974-6987 ◽  
Author(s):  
G. Vernieres ◽  
R. Kovach ◽  
C. Keppenne ◽  
S. Akella ◽  
L. Brucker ◽  
...  
Keyword(s):  
Data Assimilation ◽  
Sea Surface ◽  
Sea Surface Salinity ◽  
Surface Salinity ◽  
Ocean Data Assimilation ◽  
Salinity Data ◽  
The Impact ◽  
Data Assimilation System ◽  
Assimilation System
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