The effect of a surface data assimilation technique and the traditional four-dimensional data assimilation on the simulation of a monsoon depression over India using a mesoscale model

2007 ◽  
Vol 42 (2) ◽  
pp. 439-453 ◽  
Author(s):  
Vinodkumar ◽  
A. Chandrasekar ◽  
K. Alapaty ◽  
D. S. Niyogi
Keyword(s):  
Data Assimilation ◽  
Mesoscale Model ◽  
Monsoon Depression ◽  
Surface Data ◽  
Four Dimensional Data Assimilation ◽  
Data Assimilation Technique

The Impacts of Indirect Soil Moisture Assimilation and Direct Surface Temperature and Humidity Assimilation on a Mesoscale Model Simulation of an Indian Monsoon Depression

2008 ◽  
Vol 47 (5) ◽  
pp. 1393-1412 ◽  
Author(s):  
Vinodkumar ◽  
A. Chandrasekar ◽  
K. Alapaty ◽  
Dev Niyogi
Keyword(s):  
Soil Moisture ◽  
Data Assimilation ◽  
Surface Temperature ◽  
Mesoscale Model ◽  
Model Simulation ◽  
Monsoon Depression ◽  
Infrared Observation ◽  
Simulation Performance ◽  
Surface Data ◽  
The Impact

This study investigates the impact of the Flux-Adjusting Surface Data Assimilation System (FASDAS) and the four-dimensional data assimilation (FDDA) using analysis nudging on the simulation of a monsoon depression that formed over India during the 1999 Bay of Bengal Monsoon Experiment (BOBMEX) field campaign. FASDAS allows for the indirect assimilation/adjustment of soil moisture and soil temperature together with continuous direct surface data assimilation of surface temperature and surface humidity. Two additional numerical experiments [control (CTRL) and FDDA] were conducted to assess the relative improvements to the simulation by FASDAS. To improve the initial analysis for the FDDA and the surface data assimilation (SDA) runs, the fifth-generation Pennsylvania State University–NCAR Mesoscale Model (MM5) simulation utilized the humidity and temperature profiles from the NOAA Television Infrared Observation Satellite (TIROS) Operational Vertical Sounder (TOVS), surface winds from the Quick Scatterometer (QuikSCAT), and the conventional meteorological upper-air (radiosonde/rawinsonde, pilot balloon) and surface data. The results from the three simulations are compared with each other as well as with NCEP–NCAR reanalysis, the Tropical Rainfall Measuring Mission (TRMM), and the special buoy, ship, and radiosonde observations available during BOBMEX. As compared with the CTRL, the FASDAS and the FDDA runs resulted in (i) a relatively better-developed cyclonic circulation and (ii) a larger spatial area as well as increased rainfall amounts over the coastal regions after landfall. The FASDAS run showed a consistently improved model simulation performance in terms of reduced rms errors of surface humidity and surface temperature as compared with the CTRL and the FDDA runs.

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 Preliminary Meteorological Results of a Four-Dimensional Data Assimilation Technique in Southern Italy

2011 ◽  
Vol 01 (03) ◽  
pp. 134-141
Author(s):  
Elenio Avolio ◽  
S. Federico ◽  
A.M Sempreviva ◽  
C.R Calidonna ◽  
L. De Leo ◽  
...  
Keyword(s):  
Data Assimilation ◽  
Southern Italy ◽  
Four Dimensional Data Assimilation ◽  
Data Assimilation Technique

scholarly journals Reconstruct the Mesoscale Information of Typhoon with BDA Method Combined with AMSU-A Data Assimilation Method

2010 ◽  
Vol 2010 ◽  
pp. 1-11 ◽  
Author(s):  
Yunfeng Wang ◽  
Haiyang Zhang ◽  
Bin Wang ◽  
Yueqi Han ◽  
Xiaoping Cheng
Keyword(s):  
Data Assimilation ◽  
Mesoscale Model ◽  
Warm Start ◽  
Warm Core ◽  
Fifth Generation ◽  
Penn State ◽  
Microwave Sounding ◽  
Dynamic Forcing ◽  
Upper Level ◽  
Data Assimilation Technique

This paper conducts the assimilating experiments and simulating experiments on typhoon “Aere” (No. 0418), by use of bogus data assimilation (BDA) method combined with advanced microwave sounding unit-A (AMSU-A) data assimilation method in the fifth-generation National Center for Atmospheric Research (NCAR)/Penn State Mesoscale Model Version-3 (MM5V3), the Radiative Transfer for TIROS-N Operational Vertical Sounder Version-7 (RTTOV) model, and their adjoint models. The Bogus data constructed with BDA technique are mainly located at sea level, while the peak energy contribution levels of the sounder channels selected in AMSU-A data assimilation technique are mainly located at upper troposphere. The two types of data can reconstruct the meso-scale information and improve the typhoon initial fields under the model dynamic forcing effect, respectively from the low level and the upper level of atmosphere during the assimilating process. Numerical results show that with four-dimensional variational data assimilation (4DVAR) technique the circulation of initial fields is improved, the “warm core” of typhoon is enhanced, the “cloud water” phenomenon that occurs in the optimal initial fields and the numerical model is changed into “warm start” from “cold start”.

scholarly journals Adding Four Dimensional Data Assimilation by Analysis Nudging to the Model for Prediction Across Scales – Atmosphere (Version 4.0)

2017 ◽  
Author(s):  
Orren Russell Bullock Jr. ◽  
Hosein Foroutan ◽  
Robert C. Gilliam ◽  
Jerold A. Herwehe
Keyword(s):  
Data Assimilation ◽  
Mesoscale Model ◽  
Global Analysis ◽  
Analysis Data ◽  
Global Scale ◽  
Model Errors ◽  
Near Surface ◽  
Target Values ◽  
Four Dimensional Data Assimilation ◽  
Reference Fields

Abstract. The Model for Prediction Across Scales – Atmosphere (MPAS-A) has been modified to allow four dimensional data assimilation (FDDA) by the nudging of temperature, humidity and wind toward target values predefined on the MPAS-A computational mesh. The addition of nudging allows MPAS-A to be used as a global-scale meteorological driver for retrospective air quality modeling. The technique of analysis nudging developed for the Penn State / NCAR Mesoscale Model, and later applied in the Weather Research and Forecasting model, is implemented in MPAS-A with adaptations for its unstructured Voronoi mesh. Reference fields generated from 1° × 1° National Centers for Environmental Prediction FNL (Final) Operational Global Analysis data were used to constrain MPAS-A simulations on a 92–25 km variable-resolution mesh with refinement centered over the contiguous United States. Test simulations were conducted for January and July 2013 with and without FDDA, and compared to reference fields and near-surface meteorological observations. The results demonstrate that MPAS-A with analysis nudging has high fidelity to the reference data while still maintaining conservation of mass as in the unmodified model. The results also show that application of FDDA constrains model errors relative to 2 m temperature, 2 m water vapor mixing ratio, and 10 m wind speed such that they continue to be at or below the magnitudes found at the start of each test period.

scholarly journals Adding four-dimensional data assimilation by analysis nudging to the Model for Prediction Across Scales – Atmosphere (version 4.0)

2018 ◽  
Vol 11 (7) ◽  
pp. 2897-2922 ◽  
Author(s):  
Orren Russell Bullock Jr. ◽  
Hosein Foroutan ◽  
Robert C. Gilliam ◽  
Jerold A. Herwehe
Keyword(s):  
Data Assimilation ◽  
Mesoscale Model ◽  
Global Analysis ◽  
Analysis Data ◽  
Global Scale ◽  
Model Errors ◽  
Near Surface ◽  
Target Values ◽  
Four Dimensional Data Assimilation ◽  
Reference Fields

Abstract. The Model for Prediction Across Scales – Atmosphere (MPAS-A) has been modified to allow four-dimensional data assimilation (FDDA) by the nudging of temperature, humidity, and wind toward target values predefined on the MPAS-A computational mesh. The addition of nudging allows MPAS-A to be used as a global-scale meteorological driver for retrospective air quality modeling. The technique of “analysis nudging” developed for the Penn State/National Center for Atmospheric Research (NCAR) Mesoscale Model, and later applied in the Weather Research and Forecasting model, is implemented in MPAS-A with adaptations for its polygonal Voronoi mesh. Reference fields generated from 1∘ × 1∘ National Centers for Environmental Prediction (NCEP) FNL (Final) Operational Global Analysis data were used to constrain MPAS-A simulations on a 92–25 km variable-resolution mesh with refinement centered over the contiguous United States. Test simulations were conducted for January and July 2013 with and without FDDA, and compared to reference fields and near-surface meteorological observations. The results demonstrate that MPAS-A with analysis nudging has high fidelity to the reference data while still maintaining conservation of mass as in the unmodified model. The results also show that application of FDDA constrains model errors relative to 2 m temperature, 2 m water vapor mixing ratio, and 10 m wind speed such that they continue to be at or below the magnitudes found at the start of each test period.

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