An update on the RTTOV fast radiative transfer model (currently at version 12)

Saunders, Roger; Hocking, James; Turner, E. C.; Rayer, Peter; Rundle, David; Brunel, Pascal; Vidot, Jérôme; Rocquet, Pascale; Matricardi, Marco; Geer, Alan; Bormann, Niels; Lupu, Cristina

doi:10.5194/gmd-2018-64

Cited by 79 publications

(89 citation statements)

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“…A radiative transfer model for an observation operator was the Radiative Transfer for the Television InfraRed Observation Satellite (TIROS) Operational Vertical sounder (RTTOV) version 11.3 (Saunders et al, ; Hocking et al, ). In RTTOV 11.3, cloud scattering is calculated based on a scaling approximation (Chou et al, ) and the overlap of partial cloud is estimated with a stream method (Matricardi, ).…”

Section: Observations Model and Data Assimilation Systemmentioning

confidence: 99%

Comparison of assimilating all‐sky and clear‐sky infrared radiances from Himawari‐8 in a mesoscale system

Okamoto

Sawada

Kunii

2019

Quart J Royal Meteoro Soc

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This study examines the advantages of infrared all-sky radiance (ASR) assimilation over traditional clear-sky radiance (CSR) assimilation using a mesoscale LETKF data assimilation system. To effectively assimilate ASR data from the Himawari-8 geostationary satellite, a cloud-dependent quality-control procedure and an observation error model were developed. A single humidity band to be assimilated and thinning distance were determined based on observation error statistics. The operational pre-processing and parameter settings, such as observation errors and an adaptive bias correction for CSR, were incorporated into the LETKF data assimilation system. A comparison of the impacts of assimilating ASRs and CSRs was accomplished using single-cycle and 10-day cycle assimilation experiments. Study results revealed that ASR assimilation provided a higher degree of improvement in the first-guess fit for conventional observations and satellite retrievals with respect to temperature, moisture and wind. Furthermore, ASR assimilation displayed a more stable improvement in the prediction of a severe rainfall event because it has more universal data coverage than CSR. Adaptive bias correction schemes with two different sets of predictors for ASRs were tested and revealed the difficulty in extracting additional information for the assimilation of no-bias corrected ASR due to complicated bias factors. This was in contrast to the CSR assimilation, where bias correction had a positive impact. KEYWORDS all-sky infrared radiance, data assimilation, Himawari-8 1 Q J R Meteorol Soc. 2019;145:745-766.wileyonlinelibrary.com/journal/qj

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Section: Observations Model and Data Assimilation Systemmentioning

confidence: 99%

Comparison of assimilating all‐sky and clear‐sky infrared radiances from Himawari‐8 in a mesoscale system

Okamoto

Sawada

Kunii

2019

Quart J Royal Meteoro Soc

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“…The observation operator used in this work is the RTTOV v12 fast radiative transfer model (Saunders et al. ), which simulates the brightness temperature (TB) from atmospheric profiles of temperature, humidity, and cloud liquid water, as well as from values of surface parameters. Note that radiative transfer simulations here do not make use of cloud frozen water or cloud fraction profiles.…”

Section: Cloud‐sensitive Satellite Observationsmentioning

confidence: 99%

All‐sky satellite data assimilation of microwave temperature sounding channels at the Met Office

Migliorini

Candy

2019

Quart J Royal Meteoro Soc

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One of the main avenues to improve the skill of a numerical weather prediction system is to increase the accuracy of the initial conditions for prediction through the assimilation of a larger number of observations. This article discusses our work on assimilating cloud-affected radiances from microwave temperature sounding channels of the Advanced Microwave Sounding Unit A (AMSU-A) satellite sensor, which are currently underused by the Met Office operational data assimilation system. Despite their importance, as measured by impact indicators such as the Forecast Sensitivity to Observations Impact (FSOI), most cloud-affected microwave data are discarded so as to avoid detrimental effects, as they are much more difficult to predict than clear-sky radiances. This is due to shortcomings in parametrization schemes (for example, large-scale cloud and precipitation, convection) in both nonlinear and linearized prognostic models, as well as radiative transfer models. As first demonstrated at the European Centre for Medium-Range Weather Forecasts (ECMWF), these difficulties can be eased when observation uncertainties for cloud-affected radiances are inflated prior to assimilation as a function of cloud amount present in a given observed or predicted scene. Our results show that cloud-dependent observation uncertainty inflation for cloud-affected radiances provides beneficial effects on forecast skill over two three-month-long "all-sky" (nonprecipitating) data assimilation trials. In particular, root-mean-square error (RMSE) reductions in 500-hPa geopotential height forecasts of about 1% up to day 2 and in 10-m wind and 2-m temperature forecasts up to day 6 have been demonstrated. Also, our experiments show evidence of significant improvements in the fit between observations and short-range forecasts for lower-peaking humidity-and temperature-sensitive channels. KEYWORDSall-sky, assimilation, cloud, microwave, satellite 1The assimilation of all-sky microwave radiances in 4D-Var was pioneered at the European Centre for Medium-Range Weather Forecasts (ECMWF), where cloudand precipitation-affected radiances from the Special Sensor Microwave/Imager (SSM/I) and the Advanced Microwave Scanning Radiometer for the Earth Observing System (AMSR/E) were assimilated operationally starting from March 2009. Currently, all microwave humidity sounders (over oceans, land, snow-covered land, and sea ice) and microwave imagers (over oceans) are assimilated at ECMWF in all-sky conditions This article is published with the permission of the Controller of HMSO and the Queen's Printer for Scotland.

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“…For the simulation of ground-based and satellite observations, radiation transfer models were applied. The simulations of satellite observations were performed with the widely used fast radiative transfer model RTTOV, version 12 (Saunders et al 2018). Continuously developed and extensively validated since the 1990s, the RTTOV model now allows simulations of observations for around 90 sensors, including retired and future instruments, measuring in the IR, MW, and visible parts of the spectrum.…”

Section: Forward Simulation Of Microwave Ground-based and Infrared Samentioning

confidence: 99%

Synergy of Satellite- and Ground-Based Observations for Continuous Monitoring of Atmospheric Stability, Liquid Water Path, and Integrated Water Vapor: Theoretical Evaluations Using Reanalysis and Neural Networks

Toporov

Löhnert

2020

Journal of Applied Meteorology and Climatology

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Abstract Atmospheric stability plays an essential role in the evolution of weather events. While the upper troposphere is sampled by satellite sensors, and in situ sensors measure the atmospheric state close to the surface, only sporadic information from radiosondes or aircraft observations is available in the planetary boundary layer. Ground-based remote sensing offers the possibility to continuously and automatically monitor the atmospheric state in the boundary layer. Microwave radiometers (MWR) provide temporally resolved temperature and humidity profiles in the boundary layer and accurate values of integrated water vapor and liquid water path, and the differential absorption lidar (DIAL) measures humidity profiles with high vertical and temporal resolution up to 3000-m height. Both instruments have the potential to complement satellite observations by additional information from the lowest atmospheric layers, particularly under cloudy conditions. This study presents a neural network retrieval for stability indices, integrated water vapor, and liquid water path from simulated satellite- and ground-based measurements based on the COSMO regional reanalysis (COSMO-REA2). Focusing on the temporal resolution, the satellite-based instruments considered in the study are the currently operational Spinning Enhanced Visible and Infrared Imager (SEVIRI) and the future Infrared Sounder (IRS), both in geostationary orbit. Relative to the retrieval based on satellite observations, the additional ground-based MWR/DIAL measurements provide valuable improvements not only in the presence of clouds, which represent a limiting factor for infrared SEVIRI/IRS, but also under clear-sky conditions. The root-mean-square error for convective available potential energy, for instance, is reduced by 24% if IRS observations are complemented by ground-based MWR measurements.

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An update on the RTTOV fast radiative transfer model (currently at version 12)

Cited by 79 publications

References 0 publications

Comparison of assimilating all‐sky and clear‐sky infrared radiances from Himawari‐8 in a mesoscale system

Comparison of assimilating all‐sky and clear‐sky infrared radiances from Himawari‐8 in a mesoscale system

All‐sky satellite data assimilation of microwave temperature sounding channels at the Met Office

Synergy of Satellite- and Ground-Based Observations for Continuous Monitoring of Atmospheric Stability, Liquid Water Path, and Integrated Water Vapor: Theoretical Evaluations Using Reanalysis and Neural Networks

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