scholarly journals Simulation of Convective Initiation during IHOP_2002 Using the Flux-Adjusting Surface Data Assimilation System (FASDAS)

2006 ◽  
Vol 134 (1) ◽  
pp. 134-148 ◽  
Author(s):  
Peter P. Childs ◽  
Aneela L. Qureshi ◽  
Sethu Raman ◽  
Kiran Alapaty ◽  
Robb Ellis ◽  
...  
Keyword(s):  
Soil Moisture ◽  
Data Assimilation ◽  
Soil Temperature ◽  
Experimental Simulation ◽  
Convective Initiation ◽  
Control Simulation ◽  
Surface Data ◽  
Data Assimilation System ◽  
Surface Observations ◽  
Assimilation System

Abstract The Flux-Adjusting Surface Data Assimilation System (FASDAS) uses the surface observational analysis to directly assimilate surface layer temperature and water vapor mixing ratio and to indirectly assimilate soil moisture and soil temperature in numerical model predictions. Both soil moisture and soil temperature are important variables in the development of deep convection. In this study, FASDAS coupled within the fifth-generation Pennsylvania State University–NCAR Mesoscale Model (MM5) was used to study convective initiation over the International H2O Project (IHOP_2002) region, utilizing the analyzed surface observations collected during IHOP_2002. Two 72-h numerical simulations were performed. A control simulation was run that assimilated all available IHOP_2002 measurements into the standard MM5 four-dimensional data assimilation. An experimental simulation was also performed that assimilated all available IHOP_2002 measurements into the FASDAS version of the MM5, where surface observations were used for the FASDAS coupling. Results from this case study suggest that the use of FASDAS in the experimental simulation led to the generation of greater amounts of precipitation over a more widespread area as compared to the standard MM5 FDDA used in the control simulation. This improved performance is attributed to better simulation of surface heat fluxes and their gradients.

scholarly journals A variational data assimilation system for soil–atmosphere flux estimates for the Community Land Model (CLM3.5)

2013 ◽  
Vol 6 (4) ◽  
pp. 6605-6637
Author(s):  
C. M. Hoppe ◽  
H. Elbern ◽  
J. Schwinger
Keyword(s):  
Soil Moisture ◽  
Data Assimilation ◽  
Soil Temperature ◽  
Variational Data Assimilation ◽  
Transfer Model ◽  
Community Land Model ◽  
Soil Atmosphere ◽  
Linear Unbiased Estimate ◽  
Data Assimilation System ◽  
Assimilation System

Abstract. This article presents the development and implementation of a spatio–temporal variational data assimilation system (4D-var) for the soil–vegetation–atmosphere–transfer model "Community Land Model" (CLM3.5), along with the development of the adjoint code for the core soil-atmosphere transfer scheme of energy and soil moisture. The purpose of this work is to obtain an improved estimation technique for the energy fluxes (sensible and latent heat fluxes) between the soil and the atmosphere. Optimal assessments of these fluxes are neither available from model simulations nor measurements alone, while a 4D-var data assimilation has the potential to combine both information sources by a Best Linear Unbiased Estimate (BLUE). The 4D-var method requires the development of the adjoint model of the CLM which was established in this work. The new data assimilation algorithm is able to assimilate soil temperature and soil moisture measurements for one-dimensional columns of the model grid. Numerical experiments were first used to test the algorithm under idealised conditions. It was found that the analysis delivers improved results whenever there is a dependence between the initial values and the assimilated quantity. Furthermore, soil temperature and soil moisture from in situ field measurements were assimilated. These calculations demonstrate the improved performance of flux estimates, whenever soil property parameters are available of sufficient quality. Misspecifications could also be identified by the performance of the variational scheme.

scholarly journals A variational data assimilation system for soil–atmosphere flux estimates for the Community Land Model (CLM3.5)

2014 ◽  
Vol 7 (3) ◽  
pp. 1025-1036 ◽  
Author(s):  
C. M. Hoppe ◽  
H. Elbern ◽  
J. Schwinger
Keyword(s):  
Soil Moisture ◽  
Data Assimilation ◽  
Soil Temperature ◽  
Variational Data Assimilation ◽  
Transfer Model ◽  
Community Land Model ◽  
Soil Atmosphere ◽  
Linear Unbiased Estimate ◽  
Data Assimilation System ◽  
Assimilation System

Abstract. This paper presents the development and implementation of a spatio-temporal variational data assimilation system (4D-var) for the soil–vegetation–atmosphere transfer model "Community Land Model" (CLM3.5), along with the development of the adjoint code for the core soil–atmosphere transfer scheme of energy and soil moisture. The purpose of this work is to obtain an improved estimation technique for the energy fluxes (sensible and latent heat fluxes) between the soil and the atmosphere. Optimal assessments of these fluxes are neither available from model simulations nor measurements alone, while a 4D-var data assimilation has the potential to combine both information sources by a Best Linear Unbiased Estimate (BLUE). The 4D-var method requires the development of the adjoint model of the CLM which is established in this work. The new data assimilation algorithm is able to assimilate soil temperature and soil moisture measurements for one-dimensional columns of the model grid. Numerical experiments were first used to test the algorithm under idealised conditions. It was found that the analysis delivers improved results whenever there is a dependence between the initial values and the assimilated quantity. Furthermore, soil temperature and soil moisture from in situ field measurements were assimilated. These calculations demonstrate the improved performance of flux estimates, whenever soil property parameters are available of sufficient quality. Misspecifications could also be identified by the performance of the variational scheme.

scholarly journals A THREE-DIMENSIONAL AEROSOL VARIATIONAL DATA ASSIMILATION SYSTEM FOR AIRCRAFT AND SURFACE OBSERVATIONS

2018 ◽  
Vol XLII-3 ◽  
pp. 2215-2218
Author(s):  
Z. Zang ◽  
X. Pan ◽  
W. You ◽  
Y. Liang
Keyword(s):  
Data Assimilation ◽  
Local Time ◽  
Three Dimensional ◽  
Surface Concentration ◽  
Variational Data Assimilation ◽  
Weather Research And Forecasting ◽  
Aircraft Observations ◽  
Data Assimilation System ◽  
Surface Observations ◽  
Assimilation System

A three-dimensional variational data assimilation system is implemented within the Weather Research and Forecasting/Chemistry model, and the control variables consist of eight species of the Model for Simulation Aerosol Interactions and Chemistry scheme. In the experiments, the three-dimensional profiles of aircraft speciated observations and surface concentration observations acquired during the California Research at the Nexus of Air Quality and Climate Change field campaign are assimilated. The data assimilation experiments are performed at 02:00 local time 2 June 2010, assimilating surface observations at 02:00 and aircraft observations from 01:30 to 02:30 local time. The results show that the assimilation of both aircraft and surface observations improves the subsequent forecasts. The improved forecast skill resulting from the assimilation of the aircraft profiles persists a time longer than the assimilation of the surface observations, which suggests the necessity of vertical profile observations for extending aerosol forecasting time.

scholarly journals A Two-Season Impact Study of the WindSat Surface Wind Retrievals in the NCEP Global Data Assimilation System

2010 ◽  
Vol 25 (3) ◽  
pp. 931-949 ◽  
Author(s):  
Li Bi ◽  
James A. Jung ◽  
Michael C. Morgan ◽  
John F. Le Marshall
Keyword(s):  
Data Assimilation ◽  
Surface Wind ◽  
Temperature Fields ◽  
Experimental Simulation ◽  
Naval Research ◽  
Surface Winds ◽  
Control Simulation ◽  
Wind Retrievals ◽  
Global Data ◽  
Assimilation System

Abstract A two-season observing system experiment (OSE) was used to quantify the impacts of assimilating the WindSat surface winds product developed by the Naval Research Laboratory (NRL). The impacts of assimilating these surface winds were assessed by comparing the forecast results through 168 h for the months of October 2006 and March 2007. The National Centers for Environmental Prediction’s (NCEP) Global Data Assimilation/Global Forecast System (GDAS/GFS) was used, at a resolution of T382-64 layers, as the assimilation system and forecast model for these experiments. A control simulation utilizing all the data types assimilated in the operational GDAS was compared to an experimental simulation that added the WindSat surface winds. Quality control procedures required to assimilate the surface winds are discussed. Anomaly correlations (ACs) of geopotential heights at 1000 and 500 hPa were evaluated for the control and experiment during both seasons. The geographical distribution of the forecast impacts (FIs) on the wind field and temperature fields at 10-m height and 500 hPa is also discussed. The results of this study show that assimilating the surface wind retrievals from the WindSat satellite improve the NCEP GFS wind and temperature forecasts. A positive FI, which suggests that the error growth of the experiment is slower than the control, has been realized in the NCEP GDAS/GFS wind and temperature forecasts through 24 h. The WindSat experiment AC scores are similar to the control simulation AC scores until the day 6 forecasts, when the improvements in the WindSat experiment become greater for both seasons and in most of the cases.

scholarly journals A Soil Moisture Data Assimilation System for Pakistan Using PODEn4DVar and CLM4.5

2019 ◽  
Vol 33 (6) ◽  
pp. 1182-1193
Author(s):  
Tariq Mahmood ◽  
Zhenghui Xie ◽  
Binghao Jia ◽  
Ammara Habib ◽  
Rashid Mahmood
Keyword(s):  
Soil Moisture ◽  
Data Assimilation ◽  
Soil Moisture Data ◽  
Data Assimilation System ◽  
Assimilation System

scholarly journals The South American Land Data Assimilation System (SALDAS) 5-Yr Retrospective Atmospheric Forcing Datasets

2009 ◽  
Vol 10 (4) ◽  
pp. 999-1010 ◽  
Author(s):  
Luis Gustavo G. de Goncalves ◽  
William J. Shuttleworth ◽  
Daniel Vila ◽  
Eliane Larroza ◽  
Marcus J. Bottino ◽  
...  
Keyword(s):  
Data Assimilation ◽  
Specific Humidity ◽  
South American ◽  
Atmospheric Forcing ◽  
The South ◽  
Land Data Assimilation ◽  
Land Data Assimilation System ◽  
Data Assimilation System ◽  
Surface Observations ◽  
Assimilation System

Abstract The definition and derivation of a 5-yr, 0.125°, 3-hourly atmospheric forcing dataset that is appropriate for use in a Land Data Assimilation System operating across South America is described. Because surface observations are limited in this region, many of the variables were taken from the South American Regional Reanalysis; however, remotely sensed data were merged with surface observations to calculate the precipitation and downward shortwave radiation fields. The quality of this dataset was evaluated against the surface observations available. There are regional differences in the biases for all variables in the dataset, with volumetric biases in precipitation of the order 0–1 mm day−1 and RMSE of 5–15 mm day−1, biases in surface solar radiation of the order 10 W m−2 and RMSE of 20 W m−2, positive biases in temperature typically between 0 and 4 K depending on the region, and positive biases in specific humidity around 2–3 g kg−1 in tropical regions and negative biases around 1–2 g kg−1 farther south.

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