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2016 JCSDA Seminars


Promises and Challenges in Assimilation of Infrared and Microwave All-sky Satellite Radiances for Convection-Permitting Analysis and Prediction

Presentation file posted here when available.

Powerpoint version, (PPTX, 105.94 MB)

Speaker Fuqing Zhang
Professor, Penn State University
Date Wednesday, December 14, 2016
11:00 a.m. - 12:00 p.m.
Auditorium, NOAA Center for Weather and Climate Prediction, 5830 University Research Court, College Park, MD

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The impacts of assimilating GOES-R all-sky infrared brightness temperatures on tropical cyclone analysis and prediction were demonstrated through a series of convection-permitting observing system simulation experiments using an ensemble Kalman filter under both perfect and imperfect model scenarios. Assimilation of the high tconstrainedemporal and spatial resolution infrared radiances not only well the thermodynamic variables, including temperature, moisture and hydrometeors, but also considerably reduced analysis and forecast errors in the wind fields. The potential of all-sky radiances is further demonstrated through an additional proof-of-concept experiment assimilating real-data infrared brightness temperatures from GOES-13 and Himawara-8. An empirical flow-dependent adaptive observation error inflation (AOEI) method is proposed for assimilating all-sky satellite brightness temperatures with an ensemble Kalman filter. The AOEI method adaptively inflates the observation error when the absolute difference (innovation) between the observed and simulated brightness temperatures is greater than the square root of the combined variance of the uninflated observational error variance and ensemble-estimated background error variance. This adaptive method is designed to limit erroneous analysis increments where there are large representativeness errors, as is often the case for cloudy-affected radiance observations.

To better assimilate all-sky microwave radiance from polar- orbiting satellites, we begin to modify the Community Radiative Transfer Model (CRTM) to ensure that the cloud and precipitation particle scattering properties for calculating microwave radiances are consistent with the particle properties and size distributions internal to microphysics parameterization schemes. Using microphysics-consistent cloud scattering properties generates much greater variety in the simulated brightness temperature fields across the different microphysics schemes than the traditional use of effective radius. It is our expectation that the use of microphysics-consistent cloud scattering properties in the CRTM will help developing a more self-consistent tool for analyzing and constraining microphysics schemes, and to improve all-sky microwave radiance assimilation for convection-permitting analysis and prediction.

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James G. Yoe


All-sky IR Radiance for Himawari-8/AHI, Reflectivty Profile of GPM-Core/DPR

Summary Slides, (PDF, 9.86 MB)

Speaker Kozo Okamoto
Date Tuesday, March 8, 2016
12:00 p.m. - 1:00 p.m.
Rm2155, NOAA Center for Weather and Climate Prediction,5830 University Research Court, College Park, MD

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Although a wide variety of satellite have been used in data assimilation in NWP system, assimilating all-sky radiances and space-borne radars are still challenging. We have been studying the assimilation of all-sky IR radiances of the Advanced Himawari Imager (AHI) onboard Himawari-8 satellite and reflectivity factor profiles of the Dual Precipitation Radar (DPR) onboard Global Precipitation Mission (GPM) – Core satellite using the regional JMA's non-hydrostatic model (JMA-NHM). Recent results of these two different studies will be presented in the seminar.

Toward the all-sky AHI radiance assimilation, we are investigating the reproducibility of model simulation. The reproducibility depends on characteristics of observation, NWP model, radiative transfer model (RTM), and matching technique (e.g. interpolation, super-observation). Preliminary result on how two major fast RTMs, CRTM and RTTOV, reproduce cloud-affected radiances will be discussed.

Regarding DPR, we developed an ensemble-based variational assimilation scheme that incorporated a radar simulator and a RTM for all-sky MW radiances. We implemented single-cycle assimilation experiments including DPR and GPM MW imager (GMI) in combination or separately in a tropical cyclone case, and found that a combined use brought most accurate analysis and forecast.

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For more info, see the NCEP seminar page.

Michiko Masutani

Modified March 9, 2016 10:03 PM
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