Seminars

Seminars / Informal seminars / Lectures by ECMWF Staff and Invited Lecturers

Seminars contribute to our ongoing educational programme and are tailored to the interests of the ECMWF scientific community.

Informal seminars are held throughout the year on a range of topics. Seminars vary in their duration, depending on the area covered, and are given by subject specialists. As with the annual seminar, this may be an ECMWF staff member or an invited lecturer.

The following is a listing of seminars/lectures that have been given this year on topics of interest to the ECMWF scientific community.  See also our past informal seminars

2018

1 February
at 13:30

Room: LT

Using All-Sky Satellite Infrared Brightness Temperatures for Model Verification and in Ensemble Data Assimilation Systems

Speaker: Jason Otkin (Cooperative Institute for Meteorological Satellite Studies, University of Wisconsin-Madison, USA)

Abstract

Infrared sensors onboard geostationary satellites provide detailed information about the cloud and water vapor fields with high spatial and temporal resolutions that make them very useful for model verification and within data assimilation systems. In the first part of this talk, results will be shown from several recent studies that used GOES infrared brightness temperatures to assess the accuracy of cloud and water vapor forecasts generated by the High Resolution Rapid Refresh (HRRR) model in real-time and as part of longer-term verification studies. The real-time GOES-based verification system provides operational forecasters objective tools to quickly assess the accuracy of current and prior HRRR model forecasts when they are creating or updating their short-range forecasts. For long-term verification, the forecast accuracy is assessed using a variety of statistical methods ranging from standard grid point metrics to neighborhood-based methods such as the Fractions Skill Score to more sophisticated object-based verification tools. Overall, the results show that the simulated brightness temperatures are too warm during the first hour of the forecast, indicating that the HRRR model initialization is deficient in upper-level clouds. This warm bias, however, is quickly replaced by a large cold bias due to the rapid generation of upper-level clouds with the negative bias often lasting for several hours before the excess cloud cover dissipates. The object-based analysis showed that the HRRR initialization contains too many small cloud objects; however, the number of cloud objects becomes too low by forecast hour 2. This behavior is consistent with the changes in the simulated brightness temperatures and indicates that the forecast cloud objects become too large after a few hours.

In the second part of this talk, output from a high-resolution ensemble data assimilation system (KENDA) is used to assess the ability of a nonlinear bias correction (NBC) method that uses a Taylor series polynomial expansion of the observation-minus-background departures to remove linear and nonlinear conditional biases from all-sky SEVIRI infrared brightness temperatures. Univariate and multivariate NBC experiments were performed in which the satellite zenith angle and variables sensitive to clouds and water vapor were used as the bias correction predictors. The results showed that even though the bias of the entire error distribution is equal to zero regardless of the order of the Taylor series expansion, that there are often large conditional biases that vary as a nonlinear function of the predictor value. The linear 1st order Taylor series term had the largest impact on the entire distribution as measured by reductions in the variance; however, large conditional biases often remained across the distribution. These conditional biases were typically reduced to near zero after the nonlinear 2nd (quadratic) and 3rd (cubic) order terms were used. The results showed that variables sensitive to cloud top height are effective bias predictors especially when higher order Taylor series terms are used. Comparison of statistics compiled for clear-sky and cloudy-sky matched observations revealed that nonlinear bias corrections are more important for cloudy-sky observations as signified by the much larger impact of the 2nd and 3rd order terms on the conditional biases.

LT = Lecture Theatre, LCR = Large Committee Room, MZR = Mezzanine Committee Room,
CC = Council Chamber

 

See also - Annual seminar 2017

See also - Past Annual Seminars (under Past Workshops)

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