scholarly journals A New Variational Assimilation Method Based on Gradient Information from Satellite Data

2017 ◽  
Vol 2017 ◽  
pp. 1-12
Author(s):  
Bo Zhong ◽  
Yun-Feng Wang ◽  
Gang Ma ◽  
Xin-Yuan Ma ◽  
Lu Yang
Keyword(s):  
Data Assimilation ◽  
Satellite Data ◽  
Weather Prediction ◽  
Radiative Transfer Model ◽  
Transfer Model ◽  
Observation Data ◽  
Systematic Bias ◽  
Gradient Information ◽  
Information Assimilation ◽  
Microwave Sounding

With the development of meteorological observation technology, satellite data have found increasingly wide use in the numerical weather prediction field. However, there are various observational biases in satellite data, including a random bias brought about by complex weather systems and a systematic bias caused by the instrument itself, which greatly influence the quality of satellite data. A gradient information assimilation method is proposed in this paper to eliminate systematic bias. This method uses a gradient operator for gradient transformation between the model variable and observation variable and reaches the objective of eliminating systematic bias. An ideal experiment of variational data assimilation is conducted using the Community Radiative Transfer Model (CRTM) and Advanced Microwave Sounding Unit-A (AMSU-A) data, indicating that only assimilating gradient information can eliminate the smooth systematic bias in observation data. Then, a numerical simulation of tropical cyclone (TC) Megi and data assimilation experiment are conducted using the Weather Research Forecast (WRF) and WRF Data Assimilation (WRFDA) model as well as the Atmospheric Infrared Sounder (AIRS) data. The results show that the method of gradient information assimilation can improve the accuracy of TC tracks forecast and is also applicable for dealing with unreliable satellite data.

scholarly journals Advanced Doubling–Adding Method for Radiative Transfer in Planetary Atmospheres

2006 ◽  
Vol 63 (12) ◽  
pp. 3459-3465 ◽  
Author(s):  
Quanhua Liu ◽  
Fuzhong Weng
Keyword(s):  
Radiative Transfer ◽  
Data Assimilation ◽  
Satellite Data ◽  
Radiative Transfer Model ◽  
Transfer Model ◽  
Thermal Source ◽  
New Development ◽  
Joint Center ◽  
Computational Resources ◽  
Computation Codes

The doubling–adding method (DA) is one of the most accurate tools for detailed multiple-scattering calculations. The principle of the method goes back to the nineteenth century in a problem dealing with reflection and transmission by glass plates. Since then the doubling–adding method has been widely used as a reference tool for other radiative transfer models. The method has never been used in operational applications owing to tremendous demand on computational resources from the model. This study derives an analytical expression replacing the most complicated thermal source terms in the doubling–adding method. The new development is called the advanced doubling–adding (ADA) method. Thanks also to the efficiency of matrix and vector manipulations in FORTRAN 90/95, the advanced doubling–adding method is about 60 times faster than the doubling–adding method. The radiance (i.e., forward) computation code of ADA is easily translated into tangent linear and adjoint codes for radiance gradient calculations. The simplicity in forward and Jacobian computation codes is very useful for operational applications and for the consistency between the forward and adjoint calculations in satellite data assimilation. ADA is implemented into the Community Radiative Transfer Model (CRTM) developed at the U.S. Joint Center for Satellite Data Assimilation.

scholarly journals Long term Observations minus Background monitoring of ground-based microwave radiometer network. Part 1: Brightness Temperatures

2017 ◽  
Author(s):  
Francesco De Angelis ◽  
Domenico Cimini ◽  
Ulrich Löhnert ◽  
Olivier Caumont ◽  
Alexander Haefele ◽  
...  
Keyword(s):  
Data Assimilation ◽  
Weather Prediction ◽  
Microwave Radiometer ◽  
Scale Model ◽  
Radiative Transfer Model ◽  
Line Center ◽  
High Temporal Resolution ◽  
Transfer Model ◽  
Observation Operator ◽  
Brightness Temperatures

Abstract. Ground-based microwave radiometers (MWRs) offer the capability to provide continuous, high-temporal resolution observations of the atmospheric thermodynamic state in the planetary boundary layer (PBL) with low maintenance. This makes MWR an ideal instrument to supplement radiosonde and satellite observations when initializing numerical weather prediction (NWP) models through data assimilation. State-of-the-art data assimilation systems (e.g., variational schemes) require an accurate representation of the differences between model (background) and observations, which are then weighted by their respective errors to provide the best analysis of the true atmospheric state. In this perspective, one source of information is contained in the statistics of the differences between observations and their background counterparts (O-B). Monitoring of O-B statistics is crucial to detect and remove systematic errors coming from the measurements, the observation operator, and/or the NWP model. This work illustrates a 1-year O-B analysis for MWR observations in clear sky conditions for an European-wide network of six MWRs. Observations include MWR brightness temperatures (TB) measured by the two most common types of MWR instruments. Background profiles are extracted from the French convective scale model AROME-France before being converted into TB. The observation operator used to map atmospheric profiles into TB is the fast radiative transfer model RTTOV-gb. It is shown that O-B monitoring can effectively detect instrument malfunctions. O-B statistics (bias, standard deviation and root-mean-square) for water vapor channels (22.24–30.0 GHz) are quite consistent for all the instrumental sites, decreasing from the 22.24 GHz line center (~ 2–2.5 K) towards the high-frequency wing (~ 0.8–1.3 K). Statistics for zenith and lower elevation observations show a similar trend, though values increase with increasing air mass. O-B statistics for temperature channels show different behaviour for relatively transparent (51–53 GHz) and opaque channels (54-58 GHz). Opaque channels show lower uncertainties (

scholarly journals The Successive-Order-of-Interaction Radiative Transfer Model. Part II: Model Performance and Applications

2006 ◽  
Vol 45 (10) ◽  
pp. 1403-1413 ◽  
Author(s):  
Christopher W. O’Dell ◽  
Andrew K. Heidinger ◽  
Thomas Greenwald ◽  
Peter Bauer ◽  
Ralf Bennartz
Keyword(s):  
Radiative Transfer ◽  
Data Assimilation ◽  
Weather Prediction ◽  
Model Performance ◽  
Optimal Estimation ◽  
Radiative Transfer Model ◽  
Transfer Model ◽  
Computationally Efficient ◽  
Wide Range ◽  
Infrared Wavelengths

Abstract Radiative transfer models for scattering atmospheres that are accurate yet computationally efficient are required for many applications, such as data assimilation in numerical weather prediction. The successive-order-of-interaction (SOI) model is shown to satisfy these demands under a wide range of conditions. In particular, the model has an accuracy typically much better than 1 K for most microwave and submillimeter cases in precipitating atmospheres. Its speed is found to be comparable to or faster than the commonly used though less accurate Eddington model. An adjoint has been written for the model, and so Jacobian sensitivities can be quickly calculated. In addition to a conventional error assessment, the correlation between errors in different microwave channels is also characterized. These factors combine to make the SOI model an appealing candidate for many demanding applications, including data assimilation and optimal estimation, from microwave to thermal infrared wavelengths.

scholarly journals Satellite Data Assimilation in Numerical Weather Prediction Models. Part II: Uses of Rain-Affected Radiances from Microwave Observations for Hurricane Vortex Analysis

2007 ◽  
Vol 64 (11) ◽  
pp. 3910-3925 ◽  
Author(s):  
Fuzhong Weng ◽  
Tong Zhu ◽  
Banghua Yan
Keyword(s):  
Data Assimilation ◽  
Prediction Models ◽  
Weather Prediction ◽  
Three Dimensional ◽  
Forecast Model ◽  
Background Information ◽  
Radiative Transfer Model ◽  
Asymmetric Structure ◽  
Transfer Model ◽  
Surface Parameters

Abstract A hybrid variational scheme (HVAR) is developed to produce the vortex analysis associated with tropical storms. This scheme allows for direct assimilation of rain-affected radiances from satellite microwave instruments. In the HVAR, the atmospheric temperature and surface parameters in the storms are derived from a one-dimension variational data assimilation (1DVAR) scheme, which minimizes the cost function of both background information and satellite measurements. In the minimization process, a radiative transfer model including scattering and emission is used for radiance simulation (see Part I of this study). Through the use of 4DVAR, atmospheric temperatures from the Advanced Microwave Sounding Unit (AMSU) and surface parameters from the Advanced Microwave Scanning Radiometer (AMSR-E) are assimilated into global forecast model outputs to produce an improved analysis. This new scheme is generally applicable for variable stages of storms. In the 2005 hurricane season, the HVAR was applied for two hurricane cases, resulting in improved analyses of three-dimensional structures of temperature and wind fields as compared with operational model analysis fields. It is found that HVAR reproduces detailed structures for the hurricane warm core at the upper troposphere. Both lower-level wind speed and upper-level divergence are enhanced with reasonable asymmetric structure.

scholarly journals NOAA Operational Microwave Sounding Radiometer Data Quality Monitoring and Anomaly Assessment Using COSMIC GNSS Radio-Occultation Soundings

2020 ◽  
Vol 12 (5) ◽  
pp. 828
Author(s):  
Robbie Iacovazzi ◽  
Lin Lin ◽  
Ninghai Sun ◽  
Quanhua Liu
Keyword(s):  
Data Quality ◽  
Radio Occultation ◽  
Weather Prediction ◽  
Microwave Radiometer ◽  
Advanced Technology ◽  
Quality Monitoring ◽  
Radiative Transfer Model ◽  
Double Difference ◽  
Transfer Model ◽  
Microwave Sounding

National Oceanic and Atmospheric Administration (NOAA) operational Advanced Technology Microwave Sounder (ATMS) and Advanced Microwave Sounding Unit-A (AMSU-A) data used in numerical weather prediction and climate analysis are essential to protect life and property and maintain safe and efficient commerce. Routine data quality monitoring and anomaly assessment is important to sustain data effectiveness. One valuable parameter used to monitor microwave sounder data quality is the antenna temperature (Ta) difference (O-B) computed between direct instrument Ta measurements and forward radiative transfer model (RTM) brightness temperature (Tb) simulations. This requires microwave radiometer data to be collocated with atmospheric temperature and moisture sounding profiles, so that representative boundary conditions are used to produce the RTM-simulated Tb values. In this study, Constellation Observing System for Meteorology, Ionosphere, and Climate/Formosa Satellite Mission 3 (COSMIC) Global Navigation Satellite System (GNSS) Radio Occultation (RO) soundings over the ocean and equatorward of 60° latitude are used as input to the Community RTM (CRTM) to generate simulated NOAA-18, NOAA-19, Metop-A, and Metop-B AMSU-A and S-NPP and NOAA-20 ATMS Tb values. These simulated Tb values, together with observed Ta values that are nearly simultaneous in space and time, are used to compute Ta O-B statistics on monthly time scales for each instrument. In addition, the CRTM-simulated Tb values based on the COSMIC GNSS RO soundings can be used as a transfer standard to inter-compare Ta values from different microwave radiometer makes and models that have the same bands. For example, monthly Ta O-B statistics for NOAA-18 AMSU-A Channels 4–12 and NOAA-20 ATMS Channels 5–13 can be differenced to estimate the “double-difference” Ta biases between these two instruments for the corresponding frequency bands. This study reveals that the GNSS RO soundings are critical to monitoring and trending individual instrument O-B Ta biases and inter-instrument “double-difference” Ta biases and also to estimate impacts of some sensor anomalies on instrument Ta values.

THE JOINT CENTER FOR SATELLITE DATA ASSIMILATION

2007 ◽  
Vol 88 (3) ◽  
pp. 329-340 ◽  
Author(s):  
John Le Marshall ◽  
Louis Uccellini ◽  
Franco Einaudi ◽  
Marie Colton ◽  
Simon Chang ◽  
...  
Keyword(s):  
Data Assimilation ◽  
Satellite Data ◽  
Land Surface ◽  
Prediction Models ◽  
Radiative Transfer Model ◽  
Observation System ◽  
Transfer Model ◽  
Order Of Magnitude ◽  
Nwp Model ◽  
Joint Center

The Joint Center for Satellite Data Assimilation (JCSDA) was established by NASA and NOAA in 2001, with Department of Defense (DoD) agencies becoming partners in 2002. The goal of JCSDA is to accelerate the use of observations from Earth-orbiting satellites in operational environmental analysis and prediction models for the purpose of improving weather, ocean, climate, and air quality forecasts and the accuracy of climate datasets. Advanced instruments of current and planned satellite missions do and will increasingly provide large volumes of data related to the atmospheric, oceanic, and land surface state. During this decade, this will result in a five order of magnitude increase in the volume of data available for use by the operational and research weather, ocean, and climate communities. These data will exhibit accuracies and spatial, spectral, and temporal resolutions never before achieved. JCSDA will help ensure that the maximum benefit from investment in the space-based global observation system is realized. JCSDA will accelerate the use of satellite data from both operational and experimental spacecraft for weather and climate prediction systems. To this end, the advancement of data assimilation science by JCSDA has included the establishment of the JCSDA Community Radiative Transfer Model (CRTM), which has continual upgrades to allow for the effective use of current and many future satellite instruments. This and other activity within JCSDA have been supported by both internal and external (generally university based) research. Another key activity within JCSDA has been to lay the groundwork for and to establish common NWP model and data assimilation infrastructure for accessing new satellite data and optimizing the use of these data in operational models. As a result of this activity, common assimilation infrastructure has been established at NOAA and NASA and this will assist in a coordinated and integrated move to four-dimensional assimilation among the partner agencies. This paper discusses the establishment of JCSDA and its mission, goals, and science priorities. It also discusses recent advances made by JCSDA, and planned future developments.

Application of Principal Component Algorithm to the Direct Assimilation of AIRS in the WRF 4D-Var System

2013 ◽  
Vol 791-793 ◽  
pp. 1125-1129
Author(s):  
Yi Yu ◽  
Wei Min Zhang ◽  
Meng Bin Zhu ◽  
Min Hua Ye ◽  
Jing Sun
Keyword(s):  
Data Assimilation ◽  
Weather Prediction ◽  
Principal Component ◽  
Radiative Transfer Model ◽  
Transfer Model ◽  
Prototype System ◽  
Atmospheric Infrared Sounder ◽  
Pc Algorithm ◽  
Efficient Representation ◽  
Data Assimilation System

The use of Principal Component (PC) algorithm is explored for the efficient representation observations from high-resolution infrared sounders for the purposes of data assimilation into numerical weather prediction (NWP) models. A new version of the fast radiative transfer model has been developed that exploits principal component analysis and then implemented into the WRF 4D-Var data assimilation system, thus allow the investigation of the direct assimilation of PC scores from Atmospheric Infrared Sounder (AIRS). Testing of a prototype system where 119 AIRS spectra replaced by only 20 PC scores show significant computational saving with no detectable loss of skill in the resulting analyses or forecasts. The methodologies implemented in this regard are examined and the potential for future increased use of the data are explored.

scholarly journals Global Soil Moisture Estimation by Assimilating AMSR-E Brightness Temperatures in a Coupled CLM4–RTM–DART System

2016 ◽  
Vol 17 (9) ◽  
pp. 2431-2454 ◽  
Author(s):  
Long Zhao ◽  
Zong-Liang Yang ◽  
Timothy J. Hoar
Keyword(s):  
Soil Moisture ◽  
Data Assimilation ◽  
Global Scale ◽  
Radiative Transfer Model ◽  
Time Shift ◽  
Transfer Model ◽  
Systematic Bias ◽  
Near Surface ◽  
Community Land Model ◽  
Model Version

Abstract Very few frameworks exist that estimate global-scale soil moisture through microwave land data assimilation (DA). Toward this goal, such a framework has been developed by linking the Community Land Model, version 4 (CLM4), and a microwave radiative transfer model (RTM) with the Data Assimilation Research Testbed (DART). The deterministic ensemble adjustment Kalman filter (EAKF) within DART is utilized to estimate global multilayer soil moisture by assimilating brightness temperature observations from the Advanced Microwave Scanning Radiometer for Earth Observing System (AMSR-E). A 40-member ensemble of Community Atmosphere Model, version 4.0 (CAM4.0), reanalysis is adopted to drive CLM4 simulations. Space-specific, time-invariant microwave parameters are precalibrated to minimize uncertainties in RTM. Besides, various methods are designed to upscale AMSR-E observations for computational efficiency and time shift CAM4.0 forcing to facilitate global daily assimilations. A series of experiments are conducted to quantify the DA sensitivity to microwave parameters, choice of assimilated observations, and different CLM4 updating schemes. Evaluation results indicate that the newly established CLM4–RTM–DART framework improves the open-loop CLM4-simulated soil moisture. Precalibrated microwave parameters, rather than their default values, can ensure a more robust global-scale performance. In addition, updating near-surface soil moisture is capable of improving soil moisture in deeper layers (0–30 cm), while simultaneously updating multilayer soil moisture fails to obtain intended improvements. Future work is needed to address the systematic bias in CLM4 that cannot be fully covered through the ensemble spread in CAM4.0 reanalysis.

scholarly journals Long-term observations minus background monitoring of ground-based brightness temperatures from a microwave radiometer network

2017 ◽  
Vol 10 (10) ◽  
pp. 3947-3961 ◽  
Author(s):  
Francesco De Angelis ◽  
Domenico Cimini ◽  
Ulrich Löhnert ◽  
Olivier Caumont ◽  
Alexander Haefele ◽  
...  
Keyword(s):  
Data Assimilation ◽  
Normal Distribution ◽  
Weather Prediction ◽  
Elevation Angle ◽  
Scale Model ◽  
Radiative Transfer Model ◽  
High Temporal Resolution ◽  
Transfer Model ◽  
Observation Operator ◽  
Brightness Temperatures

Abstract. Ground-based microwave radiometers (MWRs) offer the capability to provide continuous, high-temporal-resolution observations of the atmospheric thermodynamic state in the planetary boundary layer (PBL) with low maintenance. This makes MWR an ideal instrument to supplement radiosonde and satellite observations when initializing numerical weather prediction (NWP) models through data assimilation. State-of-the-art data assimilation systems (e.g. variational schemes) require an accurate representation of the differences between model (background) and observations, which are then weighted by their respective errors to provide the best analysis of the true atmospheric state. In this perspective, one source of information is contained in the statistics of the differences between observations and their background counterparts (O–B). Monitoring of O–B statistics is crucial to detect and remove systematic errors coming from the measurements, the observation operator, and/or the NWP model. This work illustrates a 1-year O–B analysis for MWR observations in clear-sky conditions for an European-wide network of six MWRs. Observations include MWR brightness temperatures (TB) measured by the two most common types of MWR instruments. Background profiles are extracted from the French convective-scale model AROME-France before being converted into TB. The observation operator used to map atmospheric profiles into TB is the fast radiative transfer model RTTOV-gb. It is shown that O–B monitoring can effectively detect instrument malfunctions. O–B statistics (bias, standard deviation, and root mean square) for water vapour channels (22.24–30.0 GHz) are quite consistent for all the instrumental sites, decreasing from the 22.24 GHz line centre ( ∼  2–2.5 K) towards the high-frequency wing ( ∼  0.8–1.3 K). Statistics for zenith and lower-elevation observations show a similar trend, though values increase with increasing air mass. O–B statistics for temperature channels show different behaviour for relatively transparent (51–53 GHz) and opaque channels (54–58 GHz). Opaque channels show lower uncertainties (< 0.8–0.9 K) and little variation with elevation angle. Transparent channels show larger biases ( ∼  2–3 K) with relatively low standard deviations ( ∼  1–1.5 K). The observations minus analysis TB statistics are similar to the O–B statistics, suggesting a possible improvement to be expected by assimilating MWR TB into NWP models. Lastly, the O–B TB differences have been evaluated to verify the normal-distribution hypothesis underlying variational and ensemble Kalman filter-based DA systems. Absolute values of excess kurtosis and skewness are generally within 1 and 0.5, respectively, for all instrumental sites, demonstrating O–B normal distribution for most of the channels and elevations angles.

scholarly journals Assessment of Shortwave Infrared Sea Surface Reflection and Nonlocal Thermodynamic Equilibrium Effects in the Community Radiative Transfer Model Using IASI Data

2013 ◽  
Vol 30 (9) ◽  
pp. 2152-2160 ◽  
Author(s):  
Yong Chen ◽  
Yong Han ◽  
Paul van Delst ◽  
Fuzhong Weng
Keyword(s):  
Radiative Transfer ◽  
Data Assimilation ◽  
Thermodynamic Equilibrium ◽  
Weather Prediction ◽  
Radiative Transfer Model ◽  
Transfer Model ◽  
Surface Reflection ◽  
Brightness Temperatures ◽  
Temperature Retrieval ◽  
Shortwave Infrared

Abstract The nadir-viewing satellite radiances at shortwave infrared channels from 3.5 to 4.6 μm are not currently assimilated in operational numerical weather prediction data assimilation systems and are not adequately corrected for applications of temperature retrieval at daytime. For satellite observations over the ocean during the daytime, the radiance in the surface-sensitive shortwave infrared is strongly affected by the reflected solar radiance, which can contribute as much as 20.0 K to the measured brightness temperatures (BT). The nonlocal thermodynamic equilibrium (NLTE) emission in the 4.3-μm CO2 band can add a further 10 K to the measured BT. In this study, a bidirectional reflectance distribution function (BRDF) is developed for the ocean surface and an NLTE radiance correction scheme is investigated for the hyperspectral sensors. Both effects are implemented in the Community Radiative Transfer Model (CRTM). The biases of CRTM simulations to Infrared Atmospheric Sounding Interferometer (IASI) observations and the standard deviations of the biases are greatly improved during daytime (about a 1.5-K bias for NLTE channels and a 0.3-K bias for surface-sensitive shortwave channels) and are very close to the values obtained during the night. These improved capabilities in CRTM allow for effective uses of satellite data at short infrared wavelengths in data assimilation systems and in atmospheric soundings throughout the day and night.

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