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 Comparison of measured brightness temperatures from SMOS with modelled ones from ORCHIDEE and H-TESSEL over the Iberian Peninsula

2015 ◽  
Vol 12 (12) ◽  
pp. 13019-13067
Author(s):  
A. Barella-Ortiz ◽  
J. Polcher ◽  
P. de Rosnay ◽  
M. Piles ◽  
E. Gelati
Keyword(s):  
Soil Moisture ◽  
Radiative Transfer ◽  
Iberian Peninsula ◽  
Land Surface ◽  
Function Analysis ◽  
Microwave Emission ◽  
Radiative Transfer Model ◽  
Retrieval Algorithm ◽  
Transfer Model ◽  
Brightness Temperatures

Abstract. L-Band radiometry is considered to be one of the most suitable techniques to estimate surface soil moisture by means of remote sensing. Brightness temperatures are key in this process, as they are the main input in the retrieval algorithm. The work exposed compares brightness temperatures measured by the Soil Moisture and Ocean Salinity (SMOS) mission to two different sets of modelled ones, over the Iberian Peninsula from 2010 to 2012. The latter were estimated using a radiative transfer model and state variables from two land surface models: (i) ORganising Carbon and Hydrology In Dynamic EcosystEms (ORCHIDEE) and (ii) Hydrology – Tiled ECMWF Scheme for Surface Exchanges over Land (H-TESSEL). The radiative transfer model used is the Community Microwave Emission Model (CMEM). A good agreement in the temporal evolution of measured and modelled brightness temperatures is observed. However, their spatial structures are not consistent between them. An Empirical Orthogonal Function analysis of the brightness temperature's error identifies a dominant structure over the South-West of the Iberian Peninsula which evolves during the year and is maximum in Fall and Winter. Hypotheses concerning forcing induced biases and assumptions made in the radiative transfer model are analysed to explain this inconsistency, but no candidate is found to be responsible for it at the moment. Further hypotheses are proposed at the end of the paper.

scholarly journals A fast visible-wavelength 3D radiative transfer model for numerical weather prediction visualization and forward modeling

2020 ◽  
Vol 13 (6) ◽  
pp. 3235-3261
Author(s):  
Steven Albers ◽  
Stephen M. Saleeby ◽  
Sonia Kreidenweis ◽  
Qijing Bian ◽  
Peng Xian ◽  
...  
Keyword(s):  
Solar Radiation ◽  
Radiative Transfer ◽  
Numerical Weather Prediction ◽  
Land Surface ◽  
Weather Prediction ◽  
Lower Boundary ◽  
Radiative Transfer Model ◽  
Transfer Model ◽  
Numerical Weather ◽  
Visible Wavelength

Abstract. Solar radiation is the ultimate source of energy flowing through the atmosphere; it fuels all atmospheric motions. The visible-wavelength range of solar radiation represents a significant contribution to the earth's energy budget, and visible light is a vital indicator for the composition and thermodynamic processes of the atmosphere from the smallest weather scales to the largest climate scales. The accurate and fast description of light propagation in the atmosphere and its lower-boundary environment is therefore of critical importance for the simulation and prediction of weather and climate. Simulated Weather Imagery (SWIm) is a new, fast, and physically based visible-wavelength three-dimensional radiative transfer model. Given the location and intensity of the sources of light (natural or artificial) and the composition (e.g., clear or turbid air with aerosols, liquid or ice clouds, precipitating rain, snow, and ice hydrometeors) of the atmosphere, it describes the propagation of light and produces visually and physically realistic hemispheric or 360∘ spherical panoramic color images of the atmosphere and the underlying terrain from any specified vantage point either on or above the earth's surface. Applications of SWIm include the visualization of atmospheric and land surface conditions simulated or forecast by numerical weather or climate analysis and prediction systems for either scientific or lay audiences. Simulated SWIm imagery can also be generated for and compared with observed camera images to (i) assess the fidelity and (ii) improve the performance of numerical atmospheric and land surface models. Through the use of the latter in a data assimilation scheme, it can also (iii) improve the estimate of the state of atmospheric and land surface initial conditions for situational awareness and numerical weather prediction forecast initialization purposes.

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 Comparison of measured brightness temperatures from SMOS with modelled ones from ORCHIDEE and H-TESSEL over the Iberian Peninsula

2017 ◽  
Vol 21 (1) ◽  
pp. 357-375 ◽  
Author(s):  
Anaïs Barella-Ortiz ◽  
Jan Polcher ◽  
Patricia de Rosnay ◽  
Maria Piles ◽  
Emiliano Gelati
Keyword(s):  
Radiative Transfer ◽  
Iberian Peninsula ◽  
Land Surface ◽  
Function Analysis ◽  
Forthcoming Paper ◽  
Radiative Transfer Model ◽  
Retrieval Algorithm ◽  
Transfer Model ◽  
State Variables ◽  
Brightness Temperatures

Abstract. L-band radiometry is considered to be one of the most suitable techniques to estimate surface soil moisture (SSM) by means of remote sensing. Brightness temperatures are key in this process, as they are the main input in the retrieval algorithm which yields SSM estimates. The work exposed compares brightness temperatures measured by the SMOS mission to two different sets of modelled ones, over the Iberian Peninsula from 2010 to 2012. The two modelled sets were estimated using a radiative transfer model and state variables from two land-surface models: (i) ORCHIDEE and (ii) H-TESSEL. The radiative transfer model used is the CMEM. Measured and modelled brightness temperatures show a good agreement in their temporal evolution, but their spatial structures are not consistent. An empirical orthogonal function analysis of the brightness temperature's error identifies a dominant structure over the south-west of the Iberian Peninsula which evolves during the year and is maximum in autumn and winter. Hypotheses concerning forcing-induced biases and assumptions made in the radiative transfer model are analysed to explain this inconsistency, but no candidate is found to be responsible for the weak spatial correlations at the moment. Further hypotheses are proposed and will be explored in a forthcoming paper. The analysis of spatial inconsistencies between modelled and measured TBs is important, as these can affect the estimation of geophysical variables and TB assimilation in operational models, as well as result in misleading validation studies.

scholarly journals Use of Infrared Satellite Observations for the Surface Temperature Retrieval over Land in a NWP Context

2019 ◽  
Vol 11 (20) ◽  
pp. 2371 ◽  
Author(s):  
Mohamed Zied Sassi ◽  
Nadia Fourrié ◽  
Vincent Guidard ◽  
Camille Birman
Keyword(s):  
Surface Temperature ◽  
Land Surface ◽  
Weather Prediction ◽  
Satellite Observations ◽  
Radiative Transfer Model ◽  
Transfer Model ◽  
Infrared Sensors ◽  
Diurnal Cycles ◽  
Brightness Temperatures ◽  
Different Seasons

In Numerical Weather Prediction (NWP), an accurate description of surface temperature is needed to assimilate satellite observations. These observations produced by infrared and microwave sensors, help retrieving good quality land surface temperature (LST) by using surface sensitive channels and emissivity atlases. This work is a preparatory step in order to assimilate LSTs in Météo-France NWP models surface analysis. We focus on IASI and SEVIRI sensors. The first part of this work aims at comparing the SEVIRI retrieved LST to local observations from two stations included in the meso-scale AROME-France domain over four periods from different seasons. Diurnal cycles of local LST and SEVIRI LST show a good agreement especially for the summer period. Averaged biases show seasonal variability and are smaller during Winter and Autumn with less than 1 K values for both stations. The second part of the study deals with the comparison of LST values retrieved from different infrared sensors in AROME-France model. First results show encouraging agreement between both LSTs. A comparison during Autumn period for clear sky conditions reveals an almost null bias and a standard deviation of about 1.6 K. More detailed comparisons were performed over contrasted seasons with a special attention to diurnal cycles for both sensors. A better agreement is noticed during nighttime. The last step of this inter-comparison was to study the simulation of SEVIRI and IASI brightness temperatures by using a fast radiative transfer model. Thus, several simulations have been run covering various dates from different seasons by daytime and nighttime using SEVIRI LSTs, IASI LSTs and AROME-France model LSTs. Simulated brightness temperatures were then compared to observations. As expected, the best simulations are the ones using the LST retrieved from the sensor for which simulations are performed. However, the LST retrieved from another sensor provides better simulations than the predicted LST from the model especially during nighttime. For IASI simulations, SEVIRI LSTs increase RMSE by 0.2 K to 0.9 K compared to IASI LSTs for nighttime case and by around 1.5 K for daytime.

scholarly journals Brightness Temperature Simulation of Observed Precipitation Using a Three-Dimensional Radiative Transfer Model

2016 ◽  
Vol 33 (10) ◽  
pp. 2053-2064 ◽  
Author(s):  
Ian S. Adams ◽  
Michael H. Bettenhausen
Keyword(s):  
Radiative Transfer ◽  
Theoretical Study ◽  
Three Dimensional ◽  
Tropical Rainfall Measuring Mission ◽  
Radiative Transfer Model ◽  
Transfer Model ◽  
Brightness Temperatures ◽  
Roughened Surface ◽  
Emissivity Model ◽  
Surface Fields

AbstractThis study demonstrates the capabilities of a three-dimensional radiative transfer model coupled to a polarized microwave surface emissivity model. Simulations are performed at 10, 19, and 37 GHz for TMI and WindSat using three-dimensional fields of rain, snow, and graupel derived from Tropical Rainfall Measuring Mission observations of moderate Tropical Storm Asma in conjunction with atmospheric profiles and surface fields from ECMWF. Simulations are well behaved and compare well with measured brightness temperatures. Comparisons are made between simulations with a wind-roughened surface and simulations assuming a specular surface. This theoretical study, which is supported with WindSat retrievals, shows the frequencies and conditions under which surface emissions may be detected in the presence of rain.

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 A Satellite-Based Assessment of Upper-Tropospheric Water Vapor Measurements during AFWEX

2009 ◽  
Vol 48 (11) ◽  
pp. 2284-2294 ◽  
Author(s):  
Eui-Seok Chung ◽  
Brian J. Soden
Keyword(s):  
Water Vapor ◽  
Radiative Transfer Model ◽  
Transfer Model ◽  
Raman Lidar ◽  
Test Bed ◽  
Upper Troposphere ◽  
Brightness Temperatures ◽  
Radiation Test ◽  
Humidity Measurements ◽  
Atmospheric Radiation Measurement

Abstract Consistency of upper-tropospheric water vapor measurements from a variety of state-of-the-art instruments was assessed using collocated Geostationary Operational Environmental Satellite-8 (GOES-8) 6.7-μm brightness temperatures as a common benchmark during the Atmospheric Radiation Measurement Program (ARM) First International Satellite Cloud Climatology Project (ISCCP) Regional Experiment (FIRE) Water Vapor Experiment (AFWEX). To avoid uncertainties associated with the inversion of satellite-measured radiances into water vapor quantity, profiles of temperature and humidity observed from in situ, ground-based, and airborne instruments are inserted into a radiative transfer model to simulate the brightness temperature that the GOES-8 would have observed under those conditions (i.e., profile-to-radiance approach). Comparisons showed that Vaisala RS80-H radiosondes and Meteolabor Snow White chilled-mirror dewpoint hygrometers are systemically drier in the upper troposphere by ∼30%–40% relative to the GOES-8 measured upper-tropospheric humidity (UTH). By contrast, two ground-based Raman lidars (Cloud and Radiation Test Bed Raman lidar and scanning Raman lidar) and one airborne differential absorption lidar agree to within 10% of the GOES-8 measured UTH. These results indicate that upper-tropospheric water vapor can be monitored by these lidars and well-calibrated, stable geostationary satellites with an uncertainty of less than 10%, and that correction procedures are required to rectify the inherent deficiencies of humidity measurements in the upper troposphere from these radiosondes.

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