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

Abstract

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.

Assimilation of SEVIRI Water Vapor Channels With an Ensemble Kalman Filter on the Convective Scale

“…Assimilating observations, that are biased in this sense, violates basic assumptions of the ensemble Kalman filter to provide an optimal solution and thus degrades forecasts. Satellite observations are used in most meteorological centers and different observation bias correction schemes have been developed, such as variational bias correction schemes or static schemes (Eyre, 2016;Hashemi et al, 2017;Otkin and Potthast, 2019). We employ the latter type (Harris and Kelly, 2001) as a temporally adaptive scheme (Auligné et al, 2007) and apply it to ZCI-data.…”

“…It is under debate whether the satellite observation bias diagnosed as average deviation between observation and model values represents a model error or an observation error (Auligné et al, 2007), but it is likely to include both components. Since the bias correction scheme applied is valid for cloud-free observations only, occurring clouds are likely to introduce a cloud-dependent bias and may demand new cloud-dependent predictors (Otkin and Potthast, 2019).…”

“…We have applied a new clear-sky method to screen out cloud-affected observations (Harnisch et al, 2016). Several previous studies have investigated the assimilation of cloud information using satellite derived products (Schomburg et al, 2015) or a bias correction scheme (Otkin and Potthast, 2019) in a similar data assimilation setting. While those studies were done in a research context and run over a short time period, the present study targets an operational implementation involving longer periods covering variable weather situations.…”

The use of satellite and surface observations for initializing clouds in the HARMONIE NWP model

“…Recent examples of studies belonging to the first category for the convective‐scale NWP include Zhang et al . (2019), Otkin and Potthast (2019) and Scheck et al . (2020).…”

“…cloud top height, cloud mask) to adjust the initial state of the NWP model to correspond better to real atmospheric conditions. Recent examples of studies belonging to the first category for the convective-scale NWP include Zhang et al (2019), Otkin and Potthast (2019) and Scheck et al (2020). Benjamin et al (2004), Veen (2013), Chen et al (2015) and Meng et al (2019), on the other hand, represent the second category.…”