Synthetic Satellite Imagery for Real-Time High-Resolution Model Evaluation

Bikos, Dan; Lindsey, Daniel T.; Otkin, Jason A.; Sieglaff, Justin; Grasso, Louie; Siewert, Chris; Correia, James; Coniglio, Michael C.; Rabin, Robert M.; Kain, John S.; Dembek, Scott R.

doi:10.1175/waf-d-11-00130.1

Cited by 36 publications

(33 citation statements)

References 30 publications

(38 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The SOI code accounts for the assumptions made by the microphysics schemes, including each species' particle density, slope intercept parameter, and particle size distribution (e.g., MarshallPalmer, generalized gamma). Previous studies have shown that the SOI model produces accurate brightness temperatures in both clear and cloudy conditions (e.g., Otkin and Greenwald 2008;Otkin et al 2009;Bikos et al 2012) and the accuracy is expected to be within 1 K for infrared brightness temperatures (Otkin 2010).…”

Section: Synthetic Satellite Data and Forward Model Descriptionmentioning

confidence: 99%

“…The model-to-satellite approach has been used to validate and improve the accuracy of cloud microphysics schemes (Grasso and Greenwald 2004;Chaboureau and Pinty 2006;Otkin and Greenwald 2008;Grasso et al 2010;Jankov et al 2011). Synthetic satellite radiances derived from high-resolution NWP models have also been used as a proxy for future satellite sensors (Otkin et al 2007;Grasso et al 2008;Feltz et al 2009) and have been shown to be a valuable forecast tool at convective scales (Bikos et al 2012).…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Evaluating the Performance of Planetary Boundary Layer and Cloud Microphysical Parameterization Schemes in Convection-Permitting Ensemble Forecasts Using Synthetic GOES-13 Satellite Observations

Cintineo

Otkin

Xue

et al. 2014

Monthly Weather Review

View full text Add to dashboard Cite

In this study, the ability of several cloud microphysical and planetary boundary layer parameterization schemes to accurately simulate cloud characteristics within 4-km grid-spacing ensemble forecasts over the contiguous United States was evaluated through comparison of synthetic Geostationary Operational Environmental Satellite (GOES) infrared brightness temperatures with observations. Four double-moment microphysics schemes and five planetary boundary layer (PBL) schemes were evaluated. Large differences were found in the simulated cloud cover, especially in the upper troposphere, when using different microphysics schemes. Overall, the results revealed that the Milbrandt-Yau and Morrison microphysics schemes tended to produce too much upper-level cloud cover, whereas the Thompson and the Weather Research and Forecasting Model (WRF) double-moment 6-class (WDM6) microphysics schemes did not contain enough high clouds. Smaller differences occurred in the cloud fields when using different PBL schemes, with the greatest spread in the ensemble statistics occurring during and after daily peak heating hours. Results varied somewhat depending upon the verification method employed, which indicates the importance of using a suite of verification tools when evaluating high-resolution model performance. Finally, large differences between the various microphysics and PBL schemes indicate that large uncertainties remain in how these schemes represent subgrid-scale processes.

show abstract

Section: Synthetic Satellite Data and Forward Model Descriptionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Evaluating the Performance of Planetary Boundary Layer and Cloud Microphysical Parameterization Schemes in Convection-Permitting Ensemble Forecasts Using Synthetic GOES-13 Satellite Observations

Cintineo

Otkin

Xue

et al. 2014

Monthly Weather Review

View full text Add to dashboard Cite

show abstract

“…Thus, it has been used for wide variety of model evaluation studies [Morcrette, 1991;Chevallier and Kelly, 2002;Cintineo et al, 2014; and others described in section 1]. Recently, operational and forecasting research centers start producing simulated satellite IR images from highresolution mesoscale modeling, and the simulated satellite fields are available in real time and utilized by forecasters [Clark et al, 2012;Bikos et al, 2012].…”

Section: Modeling Systemsmentioning

confidence: 99%

Introducing multisensor satellite radiance-based evaluation for regional Earth System modeling

Matsui

Santanello

Shi

et al. 2014

J. Geophys. Res. Atmos.

View full text Add to dashboard Cite

Earth System modeling has become more complex, and its evaluation using satellite data has also become more difficult due to model and data diversity. Therefore, the fundamental methodology of using satellite direct measurements with instrumental simulators should be addressed especially for modeling community members lacking a solid background of radiative transfer and scattering theory. This manuscript introduces principles of multisatellite, multisensor radiance-based evaluation methods for a fully coupled regional Earth System model: NASA-Unified Weather Research and Forecasting (NU-WRF) model. We use a NU-WRF case study simulation over West Africa as an example of evaluating aerosol-cloud-precipitation-land processes with various satellite observations. NU-WRF-simulated geophysical parameters are converted to the satellite-observable raw radiance and backscatter under nearly consistent physics assumptions via the multisensor satellite simulator, the Goddard Satellite Data Simulator Unit. We present varied examples of simple yet robust methods that characterize forecast errors and model physics biases through the spatial and statistical interpretation of various satellite raw signals: infrared brightness temperature (Tb) for surface skin temperature and cloud top temperature, microwave Tb for precipitation ice and surface flooding, and radar and lidar backscatter for aerosol-cloud profiling simultaneously. Because raw satellite signals integrate many sources of geophysical information, we demonstrate user-defined thresholds and a simple statistical process to facilitate evaluations, including the infrared-microwave-based cloud types and lidar/radar-based profile classifications.

show abstract

“…More recent advances in computing resources have led to the development of more computationally expensive model-derived fields such as synthetic satellite imagery and simulated radar reflectivity [e.g., Chevallier et al (2001); Chevallier and Kelly (2002); Otkin and Greenwald (2008); Bikos et al (2012)]. Recent increases in temporal and spatial resolution of numerical weather prediction (NWP) model simulations and advances in the post-processing of the NWP output can result in simulations that realistically depict the spatial characteristics of the observed cloud features (Lee et al 2014).…”

Section: A Backgroundmentioning

confidence: 99%

Orographically induced cirrus clouds in the lee of the southern Appalachian Mountains

Ellis¹,

Blaes²,

Anderson³

2015

J. Operational Meteor.

View full text Add to dashboard Cite

The development of orographically induced cirrus clouds in the lee of the southern Appalachian Mountain chain can result in areas of unanticipated cloudiness downstream of the higher terrain across the Carolinas and Virginia. Both the degree of cloudiness and its impact on surface temperatures can reduce forecast accuracy. The general environmental conditions favorable for orographic cirrus development are known and have been qualitatively documented but to this point have not been extensively quantified. This study attempts to quantify the conditions necessary for orographic cirrus development across the southern Appalachian Mountains. Geostationary Operational Environmental Satellite imagery and atmospheric soundings are evaluated in order to better understand the most important environmental conditions needed for an orographic cirrus event to occur as well as which scenarios may produce null events. Case studies will be presented illustrating classic orographic cirrus events and their impacts on local forecast variables. Finally, best practices for operational forecasting of orographic cirrus are proposed, and the role of highresolution models in the detection of orographic cirrus events is discussed.

show abstract

Synthetic Satellite Imagery for Real-Time High-Resolution Model Evaluation

Cited by 36 publications

References 30 publications

Evaluating the Performance of Planetary Boundary Layer and Cloud Microphysical Parameterization Schemes in Convection-Permitting Ensemble Forecasts Using Synthetic GOES-13 Satellite Observations

Evaluating the Performance of Planetary Boundary Layer and Cloud Microphysical Parameterization Schemes in Convection-Permitting Ensemble Forecasts Using Synthetic GOES-13 Satellite Observations

Introducing multisensor satellite radiance-based evaluation for regional Earth System modeling

Orographically induced cirrus clouds in the lee of the southern Appalachian Mountains

Contact Info

Product

Resources

About