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 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.

scholarly journals RTTOV-gb – Adapting the fast radiative transfer model RTTOV for the assimilation of ground-based microwave radiometer observations

2016 ◽  
Author(s):  
Francesco De Angelis ◽  
Domenico Cimini ◽  
James Hocking ◽  
Pauline Martinet ◽  
Stefan Kneifel
Keyword(s):  
Radiative Transfer ◽  
Liquid Water ◽  
Weather Prediction ◽  
Microwave Radiometer ◽  
Software Tool ◽  
Radiative Transfer Model ◽  
Transfer Model ◽  
60 Ghz ◽  
Variational Assimilation ◽  
Temperature And Humidity

Abstract. Ground-based microwave radiometers (MWR) offer a new capability to provide continuous observations of the atmospheric thermodynamic state in the planetary boundary layer. Thus, they are potential candidates to supplement radiosonde network and satellite data to improve numerical weather prediction (NWP) models through a variational assimilation of their data. However in order to assimilate MWR observations a fast radiative transfer model is required and such a model is not currently available. This is necessary for going from the model state vector space to the observation space at every observation point. The fast radiative transfer model RTTOV is well accepted in the NWP community, though it was developed to simulate satellite observations only. In this work, the RTTOV code has been modified to allow for simulations of ground-based upward looking microwave sensors. In addition, the Tangent Linear, Adjoint, and K-modules of RTTOV have been adapted to provide Jacobians (i.e. the sensitivity of observations to the atmospheric thermodynamical state) for ground-based geometry. These modules are necessary for the fast minimization of the cost function in a variational assimilation scheme. The proposed ground-based version of RTTOV, called RTTOV-gb, has been validated against accurate and less time-efficient line-by-line radiative transfer models. In the frequency range commonly used for temperature and humidity profiling (22–60 GHz), root-mean-square brightness temperature differences are smaller than typical MWR uncertainties (~ 0.5 K) at all channels used in this analysis. Brightness temperatures (TB) computed with RTTOV-gb from radiosonde profiles have been compared with nearly simultaneous and colocated ground-based MWR observations. Differences between simulated and measured TB are below 0.5 K for all channels except for the water vapor band, where most of the uncertainty comes from instrumental errors. The Jacobians calculated with the K-module of RTTOV-gb have been compared with those calculated with the brute force technique and those from the line-by-line model ARTS. Jacobians are found to be almost identical, except for liquid water content Jacobians for which a 10 % difference between ARTS and RTTOV-gb at transparent channels around 450 hPa is attributed to differences in liquid water absorption models. Finally, RTTOV-gb has been applied as the forward model operator within a 1-Dimensional Variational (1D-Var) software tool in an Observing-System Simulation Experiment (OSSE). For both temperature and humidity profiles, the 1D-Var with RTTOV-gb improves the retrievals with respect to NWP model in the first few kilometers from the ground.

scholarly journals Clear-Sky Mask for the Advanced Clear-Sky Processor for Oceans

2010 ◽  
Vol 27 (10) ◽  
pp. 1609-1623 ◽  
Author(s):  
B. Petrenko ◽  
A. Ignatov ◽  
Y. Kihai ◽  
A. Heidinger
Keyword(s):  
High Resolution ◽  
Radiative Transfer ◽  
Weather Prediction ◽  
Radiative Transfer Model ◽  
Transfer Model ◽  
Clear Sky ◽  
Brightness Temperatures ◽  
Spatial Uniformity ◽  
Uniformity Test ◽  
Cross Platform

Abstract The Advanced Clear Sky Processor for Oceans (ACSPO) generates clear-sky products, such as SST, clear-sky radiances, and aerosol, from Advanced Very High Resolution Radiometer (AVHRR)-like measurements. The ACSPO clear-sky mask (ACSM) identifies clear-sky pixels within the ACSPO products. This paper describes the ACSM structure and compares the performances of ACSM and its predecessor, Clouds from AVHRR Extended Algorithm (CLAVRx). ACSM essentially employs online clear-sky radiative transfer simulations enabled within ACSPO with the Community Radiative Transfer Model (CRTM) in conjunction with numerical weather prediction atmospheric [Global Forecast System (GFS)] and SST [Reynolds daily high-resolution blended SST (DSST)] fields. The baseline ACSM tests verify the accuracy of fitting observed brightness temperatures with CRTM, check retrieved SST for consistency with Reynolds SST, and identify ambient cloudiness at the boundaries of cloudy systems. Residual cloud effects are screened out with several tests, adopted from CLAVRx, and with the SST spatial uniformity test designed to minimize misclassification of sharp SST gradients as clouds. Cross-platform and temporal consistencies of retrieved SSTs are maintained by accounting for SST and brightness temperature biases, estimated within ACSPO online and independently from ACSM. The performance of ACSM is characterized in terms of statistics of deviations of retrieved SST from the DSST. ACSM increases the amount of “clear” pixels by 30% to 40% and improves statistics of retrieved SST compared with CLAVRx. ACSM is also shown to be capable of producing satisfactory statistics of SST anomalies if the reference SST field for the exact date of observations is unavailable at the time of processing.

scholarly journals First Three Years of the Microwave Radiometer aboard Envisat: In-Flight Calibration, Processing, and Validation of the Geophysical Products

2006 ◽  
Vol 23 (6) ◽  
pp. 802-814 ◽  
Author(s):  
E. Obligis ◽  
L. Eymard ◽  
N. Tran ◽  
S. Labroue ◽  
P. Femenias
Keyword(s):  
Microwave Radiometer ◽  
Radiative Transfer Model ◽  
Altimeter Data ◽  
Transfer Model ◽  
Path Delay ◽  
Inversion Algorithm ◽  
Weather Forecasts ◽  
Brightness Temperatures ◽  
Medium Range ◽  
Satellite Altimeter Data

Abstract The Envisat microwave radiometer is designed to correct the satellite altimeter data for the excess path delay resulting from tropospheric humidity. Neural networks have been used to formulate the inversion algorithm to retrieve this quantity from the measured brightness temperatures. The learning database has been built with European Centre for Medium-Range Weather Forecasts (ECMWF) analyses and simulated brightness temperatures by a radiative transfer model. The in-flight calibration has been performed in a consistent way by adjusting measurements on simulated brightness temperatures. Finally, coincident radiosonde measurements are used to validate the Envisat wet-tropospheric correction, and this comparison shows the good performances of the method.

Characterization of Bias of Advanced Himawari Imager Infrared Observations from NWP Background Simulations Using CRTM and RTTOV

2016 ◽  
Vol 33 (12) ◽  
pp. 2553-2567 ◽  
Author(s):  
X. Zou ◽  
X. Zhuge ◽  
F. Weng
Keyword(s):  
Water Vapor ◽  
Radiative Transfer ◽  
Land Surface ◽  
Near Infrared ◽  
Weather Prediction ◽  
Radiative Transfer Model ◽  
Transfer Model ◽  
Brightness Temperatures ◽  
Emissivity Model ◽  
New Generation

AbstractStarting in 2014, the new generation of Japanese geostationary meteorological satellites carries an Advanced Himawari Imager (AHI) to provide the observations of visible, near infrared, and infrared with much improved spatial and temporal resolutions. For applications of the AHI measurements in numerical weather prediction (NWP) data assimilation systems, the biases of the AHI brightness temperatures at channels 7–16 from the model simulations are first characterized and evaluated using both the Community Radiative Transfer Model (CRTM) and the Radiative Transfer for the TIROS Operational Vertical Sounder (RTTOV). It is found that AHI biases under a clear-sky atmosphere are independent of satellite zenith angle except for channel 7. The biases of three water vapor channels increase with scene brightness temperatures and are nearly constant except at high brightness temperatures for the remaining infrared channels. The AHI biases at all the infrared channels are less than 0.6 and 1.2 K over ocean and land, respectively. The differences in biases between RTTOV and CRTM with the land surface emissivity model used in RTTOV are small except for the upper-tropospheric water vapor channels 8 and 9 and the low-tropospheric carbon dioxide channel 16. Since the inputs used for simulations are the same for CRTM and RTTOV, the differential biases at the water vapor channels may be associated with subtle differences in forward models.

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 The EUMETSAT sea ice climate record

2016 ◽  
Author(s):  
R. T. Tonboe ◽  
S. Eastwood ◽  
T. Lavergne ◽  
A. M. Sørensen ◽  
N. Rathmann ◽  
...  
Keyword(s):  
Sea Ice ◽  
Weather Prediction ◽  
Microwave Radiometer ◽  
Open Water ◽  
The Arctic ◽  
Radiative Transfer Model ◽  
Transfer Model ◽  
Sea Ice Concentration ◽  
Ice Concentration ◽  
Microwave Imager

Abstract. An Arctic and Antarctic sea ice area and extent dataset has been generated by EUMETSAT's Ocean and Sea Ice Satellite Application Facility (OSISAF) using the record of American microwave radiometer data from Nimbus 7 Scanning Multichannel Microwave radiometer (SMMR) and the Defense Meteorological satellite Program (DMSP) Special Sensor Microwave/Imager (SSM/I) and Special Sensor Microwave Imager and Sounder (SSMIS) satellite sensors. The dataset covers the period from 1978 to 2014 and updates and further developments are planned for the next phase of the project. The methodology is using: 1) numerical weather prediction (NWP) input to a radiative transfer model (RTM) for correction of the brightness temperatures for reduction of atmospheric noise, 2) dynamical algorithm tie-points to mitigate trends in residual atmospheric, sea ice and water emission characteristics and inter-sensor differences/biases, 3) and a hybrid sea ice concentration algorithm using the Bristol algorithm over ice and the Bootstrap algorithm in frequency mode over open water. A new algorithm has been developed to estimate the spatially and temporally varying sea ice concentration uncertainties. A comparison to sea ice charts from the Arctic and the Antarctic shows that ice concentrations are higher in the ice charts than estimated from the radiometer data at intermediate ice concentrations. The sea ice climate dataset is available for download at (www.osisaf.org) including documentation.

Adaptive Observation Error Inflation for Assimilating All-Sky Satellite Radiance

2017 ◽  
Vol 145 (3) ◽  
pp. 1063-1081 ◽  
Author(s):  
Masashi Minamide ◽  
Fuqing Zhang
Keyword(s):  
Radiative Transfer ◽  
System Simulation ◽  
Error Variance ◽  
Radiative Transfer Model ◽  
Transfer Model ◽  
Observation Error ◽  
Simulation Experiments ◽  
Observation Operator ◽  
Brightness Temperatures ◽  
Adaptive Observation

An empirical flow-dependent adaptive observation error inflation (AOEI) method is proposed for assimilating all-sky satellite brightness temperatures through observing system simulation experiments with an ensemble Kalman filter. The AOEI method adaptively inflates the observation error when the absolute difference (innovation) between the observed and simulated brightness temperatures is greater than the square root of the combined variance of the uninflated observational error variance and ensemble-estimated background error variance. This adaptive method is designed to limit erroneous analysis increments where there are large representativeness errors, as is often the case for cloudy-affected radiances, even if the forecast model and the observation operator (the radiative transfer model) are perfect. The promising performance of this newly proposed AOEI method is demonstrated through observing system simulation experiments assimilating all-sky brightness temperatures from GOES-R (now GOES-16) in comparison with experiments using an alternative empirical observation error inflation method proposed by Geer and Bauer. It is found that both inflation methods perform similarly in the accuracy of the analysis and in the containment of potential representativeness errors; both outperform experiments using a constant observation error without inflation. Besides being easier to implement, the empirical AOEI method proposed here also shows some advantage over the Geer–Bauer method in better updating variables at large scales. Large representative errors are likely to be compounded by unavoidable uncertainties in the forecast system and/or nonlinear observation operator (as for the radiative transfer model), in particular in the areas of moist processes, as will be the case for real-data cloudy radiances, which will be further investigated in future studies.

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