scholarly journals The Impact of Aerosols on Satellite Radiance Data Assimilation Using NCEP Global Data Assimilation System

Atmosphere ◽  
2021 ◽  
Vol 12 (4) ◽  
pp. 432
Author(s):  
Shih-Wei Wei ◽  
Cheng-Hsuan (Sarah) Lu ◽  
Quanhua Liu ◽  
Andrew Collard ◽  
Tong Zhu ◽  
...  
Keyword(s):  
Data Assimilation ◽  
Three Dimensional ◽  
Lower Atmosphere ◽  
Temperature Fields ◽  
Infrared Sensors ◽  
Research Attention ◽  
Brightness Temperatures ◽  
Thermal Window ◽  
Global Data Assimilation System ◽  
The Impact

Aerosol radiative effects have been studied extensively by climate and weather research communities. However, aerosol impacts on radiance in the context of data assimilation (DA) have received little research attention. In this study, we investigated the aerosol impacts on the assimilation of satellite radiances by incorporating time-varying three-dimensional aerosol distributions into the radiance observation operator. A series of DA experiments was conducted for August 2017. We assessed the aerosol impacts on the simulated brightness temperatures (BTs), bias correction and quality control (QC) algorithms for the assimilated infrared sensors, and analyzed temperature fields. We found that taking the aerosols into account reduces simulated BT in thermal window channels (8 to 13μm) by up to 4 K over dust-dominant regions. The cooler simulated BTs result in more positive first-guess departures, produce more negative biases, and alter the QC checks about 20%/40% of total/assimilated observations at the wavelength of 10.39μm. As a result, assimilating aerosol-affected BTs produces a warmer analyzed lower atmosphere and sea surface temperature which have better agreement with measurements over the trans-Atlantic region.

scholarly journals An OSSE-Based Evaluation of Hybrid Variational–Ensemble Data Assimilation for the NCEP GFS. Part I: System Description and 3D-Hybrid Results

2015 ◽  
Vol 143 (2) ◽  
pp. 433-451 ◽  
Author(s):  
Daryl T. Kleist ◽  
Kayo Ide
Keyword(s):  
Data Assimilation ◽  
Three Dimensional ◽  
Variational Data Assimilation ◽  
Forecast Errors ◽  
Weather Forecasts ◽  
Global Data Assimilation System ◽  
Analysis Error ◽  
Environmental Prediction ◽  
The Impact

Abstract An observing system simulation experiment (OSSE) has been carried out to evaluate the impact of a hybrid ensemble–variational data assimilation algorithm for use with the National Centers for Environmental Prediction (NCEP) global data assimilation system. An OSSE provides a controlled framework for evaluating analysis and forecast errors since a truth is known. In this case, the nature run was generated and provided by the European Centre for Medium-Range Weather Forecasts as part of the international Joint OSSE project. The assimilation and forecast impact studies are carried out using a model that is different than the nature run model, thereby accounting for model error and avoiding issues with the so-called identical-twin experiments. It is found that the quality of analysis is improved substantially when going from three-dimensional variational data assimilation (3DVar) to a hybrid 3D ensemble–variational (EnVar)-based algorithm. This is especially true in terms of the analysis error reduction for wind and moisture, most notably in the tropics. Forecast impact experiments show that the hybrid-initialized forecasts improve upon the 3DVar-based forecasts for most metrics, lead times, variables, and levels. An additional experiment that utilizes 3DEnVar (100% ensemble) demonstrates that the use of a 25% static error covariance contribution does not alter the quality of hybrid analysis when utilizing the tangent-linear normal mode constraint on the total hybrid increment.

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 The Effects of Assimilating Conventional and ATOVS Data on Forecasted Near-Surface Wind with WRF-3DVAR

2015 ◽  
Vol 143 (1) ◽  
pp. 153-164 ◽  
Author(s):  
Feimin Zhang ◽  
Yi Yang ◽  
Chenghai Wang
Keyword(s):  
Data Assimilation ◽  
Surface Wind ◽  
Lower Atmosphere ◽  
Observation Data ◽  
Initial Field ◽  
Infrared Observation ◽  
Forecast Performance ◽  
Near Surface ◽  
The Impact ◽  
Positive Effect

Abstract In this paper, the Weather Research and Forecasting (WRF) Model with the three-dimensional variational data assimilation (WRF-3DVAR) system is used to investigate the impact on the near-surface wind forecast of assimilating both conventional data and Advanced Television Infrared Observation Satellite (TIROS) Operational Vertical Sounder (ATOVS) radiances compared with assimilating conventional data only. The results show that the quality of the initial field and the forecast performance of wind in the lower atmosphere are improved in both assimilation cases. Assimilation results capture the spatial distribution of the wind speed, and the observation data assimilation has a positive effect on near-surface wind forecasts. Although the impacts of assimilating ATOVS radiances on near-surface wind forecasts are limited, the fine structure of local weather systems illustrated by the WRF-3DVAR system suggests that assimilating ATOVS radiances has a positive effect on the near-surface wind forecast under conditions that ATOVS radiances in the initial condition are properly amplified. Assimilating conventional data is an effective approach for improving the forecast of the near-surface wind.

scholarly journals Parameterizing the impact of vegetated urban canopies on wind and temperature fields by using a simple nudging method

2021 ◽  
Author(s):  
Ge Cheng ◽  
David Grawe ◽  
K. Heinke Schlünzen
Keyword(s):  
Mesoscale Model ◽  
Three Dimensional ◽  
Urban Canopy ◽  
Temperature Fields ◽  
Simple Method ◽  
Local Climate ◽  
Local Climate Zones ◽  
Urban Canopy Parameterizations ◽  
The Impact ◽  
Nudging Method

<p>Nudging is a simple method that aims to dynamically adjust the model toward the observations by including an additional feedback term in the model governing equation. This method is widely applied in data assimilation due to its simple implementation and reasonable model results. The basic concept of nudging is similar to that of urban canopy parameterization, in which additional terms are usually added in the conservation equations of momentum and energy aiming to simulate the canopy effects. However, few studies have investigated the implementation of nudging methods in urban canopy parameterizations. In this study we developed a multi-layer urban canopy parameterization (UCP) by using a nudging approach to represent the impacts of vegetated urban canopies on temperatures and winds in mesoscale models.</p><p>The difficulty of developing UCP by using a nudging method lies in defining appropriate values for the nudging coefficients and the forcing fields (e.g. indoor temperature fields for temperature nudging). To determine nudging coefficients, we use three major urban canopy morphological parameters: building height, frontal area density and building density. The ranges of these parameters are taken from the values for the Local Climate Zones datasets, in our case for the city of Hamburg. The UCP is employed in the three -dimensional atmospheric mesoscale model METRAS. Results show that this UCP can well simulate wind-blocking effects induced from obstacles as buildings and trees and urban heat island phenomenon for cities. Thus, nudging is an efficient and effective method that can be used for urban canopy parameterizations. However, as well known for nudging, it is not conserving energy. Therefore, we investigated the energy loss by tracking the reduced kinetic energy and internal energy. The UCP and model results will be presented.</p>

scholarly journals Why Scanning Instruments Are a Necessity for Constraining Temperature and Humidity Fields in the Lower Atmosphere

2014 ◽  
Vol 31 (11) ◽  
pp. 2462-2481 ◽  
Author(s):  
David Themens ◽  
Frédéric Fabry
Keyword(s):  
Water Vapor ◽  
Microwave Radiometer ◽  
Three Dimensional ◽  
Optimal Estimation ◽  
Lower Atmosphere ◽  
Temperature Fields ◽  
Vertical Profiling ◽  
Thermodynamic Variables ◽  
Free Environment ◽  
Measurement Strategies

AbstractThe ability of different ground-based measurement strategies for constraining thermodynamic variables in the troposphere, particularly at the mesoscale, is investigated. First, a preliminary assessment of the capability of pure-vertical sounders for constraining temperature and water vapor fields in clear-sky conditions to current accuracy requirements is presented. Using analyses over one month from the Rapid Refresh model as input to an optimal estimation technique, it is shown that the horizontal density of a network of nonexisting, ideal vertical profiling instruments must be greater than 30 km in order to achieve accuracies of 0.5 g kg−1 for water vapor and 0.5 K for temperature. Then, an assessment of a scanning microwave radiometer’s capability for retrieving water vapor and temperature fields in a cloud-free environment over two- and three-dimensional mesoscale domains is also presented. The information content of an elevation and azimuthal scanning microwave radiometer is assessed using the same optimal estimation framework. Even though, in any specific pointing direction, the scanning radiometer does not provide much information, it is capable of providing considerably more constraints on thermodynamic fields, particularly water vapor, than a near-perfect vertical sounder. These constraints on water vapor are largely located within 80 km of the radiometer and between 1000- and 7000-m altitude, while temperature constraints are limited to within 35 km of the instrument at altitudes between the ground and 1500 m. The findings suggest that measurements from scanning radiometers will be needed to properly constrain the temperature and especially moisture fields to accuracies needed for mesoscale forecasting.

scholarly journals Forecasting of Surface Currents via Correcting Wind Stress with Assimilation of High-Frequency Radar Data in a Three-Dimensional Model

2016 ◽  
Vol 2016 ◽  
pp. 1-12 ◽  
Author(s):  
Lei Ren ◽  
Stephen Nash ◽  
Michael Hartnett
Keyword(s):  
Data Assimilation ◽  
High Frequency ◽  
Three Dimensional ◽  
Hf Radar ◽  
Surface Velocity ◽  
Shear Stresses ◽  
Surface Currents ◽  
Dimensional Model ◽  
Three Dimensional Model ◽  
The Impact

This paper details work in assessing the capability of a hydrodynamic model to forecast surface currents and in applying data assimilation techniques to improve model forecasts. A three-dimensional model Environment Fluid Dynamics Code (EFDC) was forced with tidal boundary data and onshore wind data, and so forth. Surface current data from a high-frequency (HF) radar system in Galway Bay were used for model intercomparisons and as a source for data assimilation. The impact of bottom roughness was also investigated. Having developed a “good” water circulation model the authors sought to improve its forecasting ability through correcting wind shear stress boundary conditions. The differences in surface velocity components between HF radar measurements and model output were calculated and used to correct surface shear stresses. Moreover, data assimilation cycle lengths were examined to extend the improvements of surface current’s patterns during forecasting period, especially for north-south velocity component. The influence of data assimilation in model forecasting was assessed using a Data Assimilation Skill Score (DASS). Positive magnitude of DASS indicated that both velocity components were considerably improved during forecasting period. Additionally, the improvements of RMSE for vector direction over domain were significant compared with the “free run.”

scholarly journals Improving air quality forecasting with the assimilation of GOCI aerosol optical depth (AOD) retrievals during the KORUS-AQ period

2020 ◽  
Vol 20 (10) ◽  
pp. 6015-6036
Author(s):  
Soyoung Ha ◽  
Zhiquan Liu ◽  
Wei Sun ◽  
Yonghee Lee ◽  
Limseok Chang
Keyword(s):  
Air Quality ◽  
Data Assimilation ◽  
South Korea ◽  
Aerosol Optical Depth ◽  
Optical Depth ◽  
Three Dimensional ◽  
Upstream Region ◽  
Temporal And Spatial Distribution ◽  
The Impact ◽  
Air Quality Forecasting

Abstract. The Korean Geostationary Ocean Color Imager (GOCI) satellite has monitored the East Asian region in high temporal (e.g., hourly) and spatial resolution (e.g., 6 km) every day for the last decade, providing unprecedented information on air pollutants over the upstream region of the Korean Peninsula. In this study, the GOCI aerosol optical depth (AOD), retrieved at the 550 nm wavelength, is assimilated to enhance the quality of the aerosol analysis, thereby making systematic improvements to air quality forecasting over South Korea. For successful data assimilation, GOCI retrievals are carefully investigated and processed based on data characteristics such as temporal and spatial distribution. The preprocessed data are then assimilated in the three-dimensional variational data assimilation (3D-Var) technique for the Weather Research and Forecasting model coupled with Chemistry (WRF-Chem). For the Korea–United States Air Quality (KORUS-AQ) period (May 2016), the impact of GOCI AOD on the accuracy of surface PM2.5 prediction is examined by comparing with effects of other observations including Moderate Resolution Imaging Spectroradiometer (MODIS) sensors and surface PM2.5 observations. Consistent with previous studies, the assimilation of surface PM2.5 measurements alone still underestimates surface PM2.5 concentrations in the following forecasts, and the forecast improvements last only for about 6 h. When GOCI AOD retrievals are assimilated with surface PM2.5 observations, however, the negative bias is diminished and forecast skills are improved up to 24 h, with the most significant contributions to the prediction of heavy pollution events over South Korea.

Improving air quality forecasting with the assimilation of GOCI AOD retrievals

2020 ◽  
Author(s):  
Soyoung Ha ◽  
Zhiquan Liu
Keyword(s):  
Air Quality ◽  
Data Assimilation ◽  
South Korea ◽  
Positive Impact ◽  
Fine Particulate Matter ◽  
Three Dimensional ◽  
Basis Set ◽  
Upstream Region ◽  
The Impact ◽  
Air Quality Forecasting

<p>The Korean Geostationary Ocean Color Imager (GOCI) satellite has monitored the East Asian region in high temporal and spatial resolution every day for the last decade, providing unprecedented information on air pollutants over the upstream region of the Korean peninsula. In this study, the GOCI Aerosol optical depth (AOD), retrieved at 550 nm wavelength, is assimilated to ameliorate the analysis quality, thereby making systematic improvements on air quality forecasting in South Korea. For successful data assimilation, GOCI retrievals are carefully investigated and processed based on data characteristics. The preprocessed data are then assimilated in the three-dimensional variational data assimilation (3DVAR) technique for the Weather Research and Forecasting model coupled with Chemistry (WRF-Chem). Over the Korea-United States Air Quality (KORUS-AQ) period (May 2016), the impact of GOCI AOD on the accuracy of air quality forecasting is examined by comparing with other observations including Moderate Resolution Imaging Spectroradiometer (MODIS) sensors and fine particulate matter (PM2.5) observations at the surface. Consistent with previous studies, the assimilation of surface PM2.5 concentrations alone systematically underestimates surface PM2.5 and its positive impact lasts mainly for about 6 h. When GOCI AOD retrievals are assimilated with surface PM2.5 observations, however, the negative bias is diminished and forecasts are improved up to 24 h, with the most significant contributions to the prediction of heavy pollution events over South Korea. The talk will be finished with an introduction of our ongoing efforts on developing the assimilation capability for more sophisticated aerosol schemes such as Model for Simulating Aerosol Interactions and Chemistry (MOSAIC) and the Modal Aerosol Dynamics Model for Europe (MADE)-Volatility basis set (VBS).</p>

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.

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