scholarly journals Improvements to Cloud-Top Brightness Temperatures Computed from the CRTM at 3.9 μm

2018 ◽  
Vol 146 (11) ◽  
pp. 3927-3944 ◽  
Author(s):  
Lewis Grasso ◽  
Daniel T. Lindsey ◽  
Yoo-Jeong Noh ◽  
Christopher O’Dell ◽  
Ting-Chi Wu ◽  
...  
Keyword(s):  
Radiative Transfer ◽  
The United States ◽  
Radiative Transfer Model ◽  
Transfer Model ◽  
Brightness Temperatures ◽  
Radiance Assimilation ◽  
Weather Events ◽  
Environmental Prediction ◽  
Cloud Tops ◽  
Operational Data

ABSTRACT In preparation for all-sky satellite radiance assimilation, the Community Radiative Transfer Model (CRTM), version 2.1.3, was used to produce Geostationary Operational Environmental Satellite-12/13 (GOES-12/13) imagery near 3.9 μm. For the current study, model output simulated from different models, microphysics, and weather events was used by the CRTM to generate imagery over and near the United States. A direct comparison of observed and CRTM GOES-12/13 imagery near 3.9 μm revealed that CRTM brightness temperatures of solid-water cold cloud tops were approximately 30 K less than observed values. Two CRTM errors were identified and resolved: 1) a coding error that was found by the CRTM team and 2) incorrect optical properties of ice, resulting in improved values of brightness temperatures. Further, changes in microphysics also contributed to improvements, save for one case. The coding error solution appeared in the publicly released CRTM, version 2.3.0, on 27 November 2017, while the inclusion of the optical property solution is undetermined. Since the CRTM is the radiative transfer model within the operational data assimilation system at the National Centers for Environmental Prediction (NCEP), improvements to both the CRTM and model microphysics will be beneficial for future all-sky radiance assimilation activities.

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 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 Multiple Hydrometeors All-Sky Microwave Radiance Assimilation in FV3GFS

2020 ◽  
Vol 148 (7) ◽  
pp. 2971-2995
Author(s):  
Mingjing Tong ◽  
Yanqiu Zhu ◽  
Linjiong Zhou ◽  
Emily Liu ◽  
Ming Chen ◽  
...  
Keyword(s):  
Radiative Transfer ◽  
Positive Impact ◽  
Control Variable ◽  
Advanced Technology ◽  
Radiative Transfer Model ◽  
Transfer Model ◽  
Water Control ◽  
Microphysics Scheme ◽  
Temperature Forecast ◽  
Radiance Assimilation

Abstract Motivated by the use of the GFDL microphysics scheme in the Finite-Volume Cubed-Sphere Dynamical Core Global Forecast System (FV3GFS), the all-sky radiance assimilation framework has been expanded to include precipitating hydrometeors. Adding precipitating hydrometeors allows the assimilation of precipitation-affected radiance in addition to cloudy radiance. In this upgraded all-sky framework, the five hydrometeors, including cloud liquid water, cloud ice, rain, snow, and graupel, are the new control variables, replacing the original cloud water control variable. The Community Radiative Transfer Model (CRTM) was interfaced with the newly added precipitating hydrometeors. Subgrid cloud variability was considered by using the average cloud overlap scheme. Multiple scattering radiative transfer was activated in the upgraded framework. Radiance observations from the Advanced Microwave Sounding Unit-A (AMSU-A) and the Advanced Technology Microwave Sounder (ATMS) over ocean were assimilated in all-sky approach. This new constructed all-sky framework shows neutral to positive impact on overall forecast skill. Improvement was found in 500-hPa geopotential height forecast in both Northern and Southern Hemispheres. Temperature forecast was also improved at 850 hPa in the Southern Hemisphere and the tropics.

scholarly journals Development of an OMI AI data assimilation scheme for aerosol modeling over bright surfaces – a step toward direct radiance assimilation in the UV spectrum

2020 ◽  
Author(s):  
Jianglong Zhang ◽  
Robert J. D. Spurr ◽  
Jeffrey S. Reid ◽  
Peng Xian ◽  
Peter R. Colarco ◽  
...  
Keyword(s):  
Radiative Transfer ◽  
Data Assimilation ◽  
Radiative Transfer Model ◽  
Transfer Model ◽  
Uv Spectrum ◽  
Model Simulations ◽  
Monitoring Instrument ◽  
Radiance Assimilation ◽  
Data Assimilation Scheme ◽  
Assimilation Scheme

Abstract. Using the Vector LInearized Discrete Ordinate Radiative Transfer (VLIDORT) code as the main driver for forward model simulations, a first-of-its-kind data assimilation scheme has been developed for assimilating Ozone Monitoring Instrument (OMI) aerosol index (AI) measurements into the Naval Aerosol Analysis and Predictive System (NAAPS). This study suggests both RMSE and absolute errors can be significantly reduced in NAAPS analyses with the use of OMI AI data assimilation, when compared to values from NAAPS natural runs. Improvements in model simulations demonstrate the utility of OMI AI data assimilation for improving the accuracy of aerosol model analysis over cloudy regions and bright surfaces. However, the OMI AI data assimilation alone does not out-perform aerosol data assimilation that uses passive-based aerosol optical depth (AOD) products over cloud free skies and dark surfaces. Further, as AI assimilation requires the deployment of a fully-multiple-scatter-aware radiative transfer model in the forward simulations, computational burden is an issue. Nevertheless, the newly-developed modeling system contains the necessary ingredients for assimilation of radiances in the ultra-violet (UV) spectrum, and our study shows the potential of direct radiance assimilation at both UV and visible spectrums, possibly coupled with AOD assimilation, for aerosol applications in the future. Additional data streams can be added, including data from TROPOspheric Monitoring Instrument (TROPOMI), Ozone Mapping and Profiler Suite (OMPS) and eventually with the Plankton, Aerosol, Cloud and ocean Ecosystem (PACE) mission.

scholarly journals Radiative Transfer Simulations Using Mesoscale Cloud Model Outputs: Comparisons with Passive Microwave and Infrared Satellite Observations for Midlatitudes

2007 ◽  
Vol 64 (5) ◽  
pp. 1550-1568 ◽  
Author(s):  
Ingo Meirold-Mautner ◽  
Catherine Prigent ◽  
Eric Defer ◽  
Juan R. Pardo ◽  
Jean-Pierre Chaboureau ◽  
...  
Keyword(s):  
Radiative Transfer ◽  
Large Scale ◽  
Mesoscale Model ◽  
Cloud Model ◽  
Radiative Transfer Model ◽  
Transfer Model ◽  
Specific Situation ◽  
Dynamical Structure ◽  
Atmospheric Transmission ◽  
Brightness Temperatures

Abstract Real midlatitude meteorological cases are simulated over western Europe with the cloud mesoscale model Méso-NH, and the outputs are used to calculate brightness temperatures at microwave frequencies with the Atmospheric Transmission at Microwave (ATM) radiative transfer model. Satellite-observed brightness temperatures (TBs) from the Advanced Microwave Scanning Unit B (AMSU-B) and the Special Sensor Microwave Imager (SSM/I) are compared to the simulated ones. In this paper, one specific situation is examined in detail. The infrared responses have also been calculated and compared to the Meteosat coincident observations. Overall agreement is obtained between the simulated and the observed brightness temperatures in the microwave and in the infrared. The large-scale dynamical structure of the cloud system is well captured by Méso-NH. However, in regions with large quantities of frozen hydrometeors, the comparison shows that the simulated microwave TBs are higher than the measured ones in the window channels at high frequencies, indicating that the calculation does not predict enough scattering. The factors responsible for the scattering (frozen particle distribution, calculation of particle dielectric properties, and nonsphericity of the particles) are analyzed. To assess the quality of the cloud and precipitation simulations by Méso-NH, the microphysical fields predicted by the German Lokal-Modell are also considered. Results show that in these midlatitude situations, the treatment of the snow category has a high impact on the simulated brightness temperatures. The snow scattering parameters are tuned to match the discrete dipole approximation calculations and to obtain a good agreement between simulations and observations even in the areas with significant frozen particles. Analysis of the other meteorological simulations confirms these results. Comparing simulations and observations in the microwave provides a powerful evaluation of resolved clouds in mesoscale models, especially the precipitating ice phase.

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.

Monitoring of IR Clear-Sky Radiances over Oceans for SST (MICROS)

2011 ◽  
Vol 28 (10) ◽  
pp. 1228-1242 ◽  
Author(s):  
Xingming Liang ◽  
Alexander Ignatov
Keyword(s):  
Information Service ◽  
Radiative Transfer Model ◽  
Transfer Model ◽  
Web Based ◽  
Clear Sky ◽  
Brightness Temperatures ◽  
Ongoing Work ◽  
Double Differences ◽  
Environmental Prediction ◽  
Initial Objective

Abstract Monitoring of IR Clear-Sky Radiances over Oceans for SST (MICROS) is a Web-based tool to monitor “model minus observation” (M − O) biases in clear-sky brightness temperatures (BTs) and sea surface temperatures (SSTs) produced by the Advanced Clear-Sky Processor for Oceans (ACSPO). Currently, MICROS monitors M − O biases in three Advanced Very High Resolution Radiometer (AVHRR) bands centered at 3.7, 11, and 12 μm for five satellites, NOAA-16, -17, -18, -19 and Meteorological Operational (MetOp)-A. The fast Community Radiative Transfer Model (CRTM) is employed to simulate clear-sky BTs, using Reynolds SST and National Centers for Environmental Prediction Global Forecast System profiles as input. Simulated BTs are used in ACSPO for improving cloud screening, physical SST inversions, and monitoring and validating satellite BTs. The key MICROS objectives are to fully understand and reconcile CRTM and AVHRR BTs, and to minimize cross-platform biases through improvements to ACSPO algorithms, CRTM and its inputs, satellite radiances, and skin-bulk and diurnal SST modeling. Initially, MICROS was intended for internal use within the National Environmental Satellite, Data, and Information Service (NESDIS) SST team for testing and improving ACSPO products. However, it has quickly outgrown this initial objective and is now used by several research and applications groups. In particular, inclusion of double differences in MICROS has contributed to sensor-to-sensor monitoring within the Global Space-Based Intercalibration System, which is customarily performed using the well-established simultaneous nadir overpass technique. Also, CRTM scientists have made a number of critical improvements to CRTM using MICROS results. They now routinely use MICROS to continuously monitor M − O biases and validate and improve CRTM performance. MICROS is also instrumental in evaluating the accuracy of the first-guess SST and upper-air fields used as input to CRTM. This paper gives examples of these applications and discusses ongoing work and future plans.

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