Improvements on the ice cloud modeling capabilities of the Community Radiative Transfer Model

Yi, Bingqi; Yang, Ping; Liu, Quanhua; Delst, Paul van; Boukabara, Sid-Ahmed; Weng, Fuzhong

doi:10.1002/2016jd025207

Cited by 29 publications

(21 citation statements)

References 57 publications

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“…This point of emphasis was not made because cloud-impacted observations were deemed unimportant, but rather, was due to the difficulty of using them in existing DA systems (Errico et al 2007). Indeed, until the recent development of all-sky DA methods, the need to exclude observations impacted by clouds and precipitation meant that only a small percentage of available satellite observations were actively assimilated at global NWP centers (Yang et al 2016). This limitation is even more severe for regional-scale NWP models where the entire domain may be covered by clouds (Lin et al 2017).…”

Section: Introductionmentioning

confidence: 99%

“…More recently, the National Centers for Environmental Prediction has also started to assimilate all-sky microwave observations in their operational global forecasting system (Zhu et al 2016). Numerous studies have documented the benefits of assimilating all-sky microwave observations in global and regional modeling systems (e.g., Aonashi and Eito 2011; Geer et al 2014;Yang et al 2016;Kazumori et al 2016;Baordo and Geer 2016;Zhang and Guan 2017;Lawrence et al 2018;Wu et al 2019).…”

Section: Introductionmentioning

confidence: 99%

“…For example, deficiencies in the forward radiative transfer model used to compute the model-equivalent brightness temperatures, or the inability of the parameterization schemes in the NWP model to accurately represent the spatial extent, thickness, and optical properties of clouds, can introduce systematic errors that vary as a nonlinear function of some cloud property, such as cloud top height (Dee 2005;Dee and Uppala 2009;Mahfouf 2010;Otkin and Greenwald 2008;Cintineo et al 2014;Eikenberg et al 2015). Though the accuracy of radiative transfer models has improved greatly in recent years, there are still large uncertainties regarding the specification of cloud properties, especially for ice clouds (Yang et al 2013;Baum et al 2014;Yi et al 2016).…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Assimilation of All-Sky SEVIRI Infrared Brightness Temperatures in a Regional-Scale Ensemble Data Assimilation System

Otkin

Potthast

2019

Monthly Weather Review

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Ensemble data assimilation experiments were performed to assess the ability of satellite all-sky infrared brightness temperatures and different bias correction (BC) predictors to improve the accuracy of short-range forecasts used as the model background during each assimilation cycle. Satellite observations sensitive to clouds and water vapor in the upper troposphere were assimilated at hourly intervals during a 3-day period. Linear and nonlinear conditional biases were removed from the infrared observations using a Taylor series polynomial expansion of the observation-minus-background departures and BC predictors sensitive to clouds and water vapor or to variations in the satellite zenith angle. Assimilating the all-sky infrared brightness temperatures without BC degraded the forecast accuracy based on comparisons to radiosonde observations. Removal of the linear and nonlinear conditional biases from the satellite observations substantially improved the results, with predictors sensitive to the location of the cloud top having the largest impact, especially when higher-order nonlinear BC terms were used. Overall, experiments employing the observed cloud-top height or observed brightness temperature as the bias predictor had the smallest water vapor, cloud, and wind speed errors, while also having less degradation to temperatures than occurred when using other predictors. The forecast errors were smaller during these experiments because the cloud-height-sensitive BC predictors were able to more effectively remove the large conditional biases for lower brightness temperatures associated with a deficiency in upper-level clouds in the model background.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Assimilation of All-Sky SEVIRI Infrared Brightness Temperatures in a Regional-Scale Ensemble Data Assimilation System

Otkin

Potthast

2019

Monthly Weather Review

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“…A gamma size distribution with an effective variance of 0.1 (Hansen and Travis, 1974) is assumed to compute the bulk scattering properties (i.e., the extinction coefficient, single-scattering albedo, asymmetry factor and phase function coefficients). Comparisons between the simulation and observation show that the CRTM with new cloud optical property LUTs substantially improves the 220 simulation on cloudy atmospheres (Yi et al, 2016;Yao et al, 2018).…”

Section: Radiance-based Evaluationmentioning

confidence: 93%

Assessment of cloud properties from the reanalysis with satellite observations over East Asia

Yao

Liu

Yin

et al. 2019

Preprint

Self Cite

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Abstract. Extensive observational and numerical investigations have been performed to better characterize cloud properties. However, due to the large variations of cloud spatiotemporal distributions and physical properties, quantitative depictions of clouds in different atmospheric reanalysis datasets are still highly uncertain, and cloud parameters in the models to produce those datasets remain largely unconstrained. A radiance-based evaluation approach is introduced and performed to assess the quality of cloud properties by directly comparing reanalysis-driven forward radiative transfer results with radiances from satellite observation. The newly developed China Meteorological Administration Reanalysis data (CRA), the ECMWF’s Fifth-generation Reanalysis (ERA5), and the Modern-Era Retrospective Analysis for Applications, Version 2 (MERRA-2) are considered in the present study. To avoid the unrealistic assumptions and uncertainties on satellite retrieval algorithms and products, the radiative transfer model (RTM) is used as a bridge to “translate” the reanalysis to corresponding satellite observations. The simulated reflectance and brightness temperatures (BTs) are directly compared with observations from the Advanced Himawari Imager (AHI) onboard the Himawari-8 satellite in the region from 80° E to 160° W between 60° N and 60° S, especially for results over East Asia. Comparisons of the reflectance in the solar and BTs in the infrared (IR) window channels reveal that CRA reanalysis better represents the total cloud cover than the other two reanalysis datasets. The simulated BTs for CRA and ERA5 are close to each other in many pixels, whereas the vertical distributions of cloud properties are significantly different, and ERA5 depicts a better deep convection structure than CRA reanalysis. Comparisons of the BT differences (BTDs) between the simulations and observations suggest that the water clouds are generally overestimated in ERA5 and MERRA-2, whereas the ice cloud is responsible for the overestimation over the center of cyclones in ERA5. Overall, the cloud from CRA, ERA5, and MERRA-2 show their own advantages in different aspects. The ERA5 reanalysis is found the most capability in representing the cloudy atmosphere over East Asia, and the results in CRA are close to those in ERA5.

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“…For water clouds, the biases in the IR window channels may reach 2 K for optical thin clouds. BTDs in the water vapor channels are within ±1 K. Moreover, compared to the default CRTM model, the updated model can substantially improve CRTM simulations of cloudy atmospheres (Yi et al, 2016;.…”

Section: B Yao Et Al: Evaluation Of Cloud Propertiesmentioning

confidence: 93%

Evaluation of cloud properties from reanalyses over East Asia with a radiance-based approach

et al. 2020

Self Cite

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Abstract. Extensive observational and numerical investigations have been performed to better characterize cloud properties. However, due to the large variations in cloud spatiotemporal distributions and physical properties, quantitative depictions of clouds in different atmospheric reanalysis datasets are still highly uncertain. A radiance-based evaluation approach is introduced and performed to evaluate the quality of cloud properties from reanalysis datasets. The China Meteorological Administration reanalysis (CRA); the ECMWF fifth-generation reanalysis (ERA5); and the Modern-Era Retrospective analysis for Applications, Version 2 (MERRA-2), i.e., those reanalyses providing sufficient cloud information, are considered. To avoid the influence of assumptions and uncertainties on satellite retrieval algorithms, forward radiative transfer simulations are used as a bridge to translate the reanalyses to corresponding radiances that are expected to be observed by satellites. The simulated reflectances and brightness temperatures (BTs) are directly compared with observations from the Advanced Himawari Imager onboard the Himawari-8 satellite in the East Asia region. We find that the simulated reflectances and BTs based on CRA and ERA5 are close to each other. CRA represents the total and midlayer cloud cover better than the other two datasets, and ERA5 depicts deep-convection structures more closely than CRA does. Comparisons of the simulated and observed BT differences suggest that water clouds are generally overestimated in ERA5 and MERRA-2, and MERRA-2 also overestimates the ice clouds over cyclone centers. Overall, clouds from CRA, ERA5, and MERRA-2 show their own advantages in different aspects. The ERA5 reanalysis has the best capability to represent the cloudy atmospheres over East Asia, and the CRA representations are close to those in ERA5.

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Improvements on the ice cloud modeling capabilities of the Community Radiative Transfer Model

Cited by 29 publications

References 57 publications

Assimilation of All-Sky SEVIRI Infrared Brightness Temperatures in a Regional-Scale Ensemble Data Assimilation System

Assimilation of All-Sky SEVIRI Infrared Brightness Temperatures in a Regional-Scale Ensemble Data Assimilation System

Assessment of cloud properties from the reanalysis with satellite observations over East Asia

Evaluation of cloud properties from reanalyses over East Asia with a radiance-based approach

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