Bingying Shi

The sensitivity of all-sky infrared radiances to both hydrometeor content and cloud height leads to a very non-Gaussian distribution of first-guess departures. This non-Gaussianity can be mitigated by the application of cloud-dependent error models that normalize departures by an estimate of the cloud-height effect via assigning increased errors in situations with high clouds that can lead to very large departures. In the current study, we systematically evaluate existing error models and propose a revised approach that leads to a better fit to a Gaussian distribution at no additional cost. Furthermore, the revised approach is physically better justified as the cloud effect is estimated by the maximum cloud effect of model and observations, which determines the largest possible departure. Preceding studies, in contrast, used the mean cloud effect of model and observations. Our study is based on a one-month data set of infrared observations in two water vapor channels (6.2 and 7.3 μm) from the Spinning Enhanced Visible and Infrared Imager (SEVIRI) onboard the Meteosat Second Generation satellite and corresponding simulations from the weather forecast model AROME (Application of Research to Operations at Mesoscale) over central Europe. The evaluation of different approaches revealed that near-Gaussian departures can be achieved with three different approaches for the estimation of the cloud effect: (1) deviation from a climatologically estimated value; (2) deviation from the clear-sky brightness temperature of a window channel; and (3) deviation from the clear-sky brightness temperature of the channel that is assimilated. For the lower-peaking channel (7.3 μm) with a larger cloud effect, best results were achieved with the first two options. For the 6.2 μm channel, the third option led to a slightly more Gaussian distribution. The third option, however, requires a quality control that eliminates about 10 % of the observations.

The sensitivity of all-sky infrared radiances to both hydrometeor content and cloud height leads to a very non-Gaussian distribution of first-guess departures. This non-Gaussianity can be mitigated by the application of cloud-dependent error models that normalize departures by an estimate of the cloud-height effect via assigning increased errors in situations with high clouds that can lead to very large departures. In the current study, we systematically evaluate existing error models and propose a revised approach that leads to a better fit to a Gaussian distribution at no additional cost. Furthermore, the revised approach is physically better justified as the cloud effect is estimated by the maximum cloud effect of model and observations, which determines the largest possible departure. Preceding studies, in contrast, used the mean cloud effect of model and observations. Our study is based on a one-month data set of infrared observations in two water vapor channels (6.2 and 7.3 μm) from the Spinning Enhanced Visible and Infrared Imager (SEVIRI) onboard the Meteosat Second Generation satellite and corresponding simulations from the weather forecast model AROME (Application of Research to Operations at Mesoscale) over central Europe. The evaluation of different approaches revealed that near-Gaussian departures can be achieved with three different approaches for the estimation of the cloud effect: (1) deviation from a climatologically estimated value; (2) deviation from the clear-sky brightness temperature of a window channel; and (3) deviation from the clear-sky brightness temperature of the channel that is assimilated. For the lower-peaking channel (7.3 μm) with a larger cloud effect, best results were achieved with the first two options. For the 6.2 μm channel, the third option led to a slightly more Gaussian distribution. The third option, however, requires a quality control that eliminates about 10 % of the observations.

The sensitivity of all-sky infrared radiances to both hydrometeor content and cloud height leads to a very non-Gaussian distribution of first-guess departures. This non-Gaussianity can be mitigated by the application of cloud-dependent error models that normalize departures by an estimate of the cloud-height effect via assigning increased errors in situations with high clouds that can lead to very large departures. In the current study, we systematically evaluate existing error models and propose a revised approach that leads to a better fit to a Gaussian distribution at no additional cost. Furthermore, the revised approach is physically better justified as the cloud effect is estimated by the maximum cloud effect of model and observations, which determines the largest possible departure. Preceding studies, in contrast, used the mean cloud effect of model and observations. Our study is based on a one-month data set of infrared observations in two water vapor channels (6.2 and 7.3 μm) from the Spinning Enhanced Visible and Infrared Imager (SEVIRI) onboard the Meteosat Second Generation satellite and corresponding simulations from the weather forecast model AROME (Application of Research to Operations at Mesoscale) over central Europe. The evaluation of different approaches revealed that near-Gaussian departures can be achieved with three different approaches for the estimation of the cloud effect: (1) deviation from a climatologically estimated value; (2) deviation from the clear-sky brightness temperature of a window channel; and (3) deviation from the clear-sky brightness temperature of the channel that is assimilated. For the lower-peaking channel (7.3 μm) with a larger cloud effect, best results were achieved with the first two options. For the 6.2 μm channel, the third option led to a slightly more Gaussian distribution. The third option, however, requires a quality control that eliminates about 10 % of the observations.

The sensitivity of all-sky infrared radiances to both hydrometeor content and cloud height leads to a very non-Gaussian distribution of first-guess departures. This non-Gaussianity can be mitigated by the application of cloud-dependent error models that normalize departures by an estimate of the cloud-height effect via assigning increased errors in situations with high clouds that can lead to very large departures. In the current study, we systematically evaluate existing error models and propose a revised approach that leads to a better fit to a Gaussian distribution at no additional cost. Furthermore, the revised approach is physically better justified as the cloud effect is estimated by the maximum cloud effect of model and observations, which determines the largest possible departure. Preceding studies, in contrast, used the mean cloud effect of model and observations. Our study is based on a one-month data set of infrared observations in two water vapor channels (6.2 and 7.3 μm) from the Spinning Enhanced Visible and Infrared Imager (SEVIRI) onboard the Meteosat Second Generation satellite and corresponding simulations from the weather forecast model AROME (Application of Research to Operations at Mesoscale) over central Europe. The evaluation of different approaches revealed that near-Gaussian departures can be achieved with three different approaches for the estimation of the cloud effect: (1) deviation from a climatologically estimated value; (2) deviation from the clear-sky brightness temperature of a window channel; and (3) deviation from the clear-sky brightness temperature of the channel that is assimilated. For the lower-peaking channel (7.3 μm) with a larger cloud effect, best results were achieved with the first two options. For the 6.2 μm channel, the third option led to a slightly more Gaussian distribution. The third option, however, requires a quality control that eliminates about 10 % of the observations.

The sensitivity of all-sky infrared radiances to both hydrometeor content and cloud height leads to a very non-Gaussian distribution of first-guess departures. This non-Gaussianity can be mitigated by the application of cloud-dependent error models that normalize departures by an estimate of the cloud-height effect via assigning increased errors in situations with high clouds that can lead to very large departures. In the current study, we systematically evaluate existing error models and propose a revised approach that leads to a better fit to a Gaussian distribution at no additional cost. Furthermore, the revised approach is physically better justified as the cloud effect is estimated by the maximum cloud effect of model and observations, which determines the largest possible departure. Preceding studies, in contrast, used the mean cloud effect of model and observations. Our study is based on a one-month data set of infrared observations in two water vapor channels (6.2 and 7.3 μm) from the Spinning Enhanced Visible and Infrared Imager (SEVIRI) onboard the Meteosat Second Generation satellite and corresponding simulations from the weather forecast model AROME (Application of Research to Operations at Mesoscale) over central Europe. The evaluation of different approaches revealed that near-Gaussian departures can be achieved with three different approaches for the estimation of the cloud effect: (1) deviation from a climatologically estimated value; (2) deviation from the clear-sky brightness temperature of a window channel; and (3) deviation from the clear-sky brightness temperature of the channel that is assimilated. For the lower-peaking channel (7.3 μm) with a larger cloud effect, best results were achieved with the first two options. For the 6.2 μm channel, the third option led to a slightly more Gaussian distribution. The third option, however, requires a quality control that eliminates about 10 % of the observations.

The sensitivity of all-sky infrared radiances to both hydrometeor content and cloud height leads to a very non-Gaussian distribution of first-guess departures. This non-Gaussianity can be mitigated by the application of cloud-dependent error models that normalize departures by an estimate of the cloud-height effect via assigning increased errors in situations with high clouds that can lead to very large departures. In the current study, we systematically evaluate existing error models and propose a revised approach that leads to a better fit to a Gaussian distribution at no additional cost. Furthermore, the revised approach is physically better justified as the cloud effect is estimated by the maximum cloud effect of model and observations, which determines the largest possible departure. Preceding studies, in contrast, used the mean cloud effect of model and observations. Our study is based on a one-month data set of infrared observations in two water vapor channels (6.2 and 7.3 μm) from the Spinning Enhanced Visible and Infrared Imager (SEVIRI) onboard the Meteosat Second Generation satellite and corresponding simulations from the weather forecast model AROME (Application of Research to Operations at Mesoscale) over central Europe. The evaluation of different approaches revealed that near-Gaussian departures can be achieved with three different approaches for the estimation of the cloud effect: (1) deviation from a climatologically estimated value; (2) deviation from the clear-sky brightness temperature of a window channel; and (3) deviation from the clear-sky brightness temperature of the channel that is assimilated. For the lower-peaking channel (7.3 μm) with a larger cloud effect, best results were achieved with the first two options. For the 6.2 μm channel, the third option led to a slightly more Gaussian distribution. The third option, however, requires a quality control that eliminates about 10 % of the observations.

The sensitivity of all-sky infrared radiances to both hydrometeor content and cloud height leads to a very non-Gaussian distribution of first-guess departures. This non-Gaussianity can be mitigated by the application of cloud-dependent error models that normalize departures by an estimate of the cloud-height effect via assigning increased errors in situations with high clouds that can lead to very large departures. In the current study, we systematically evaluate existing error models and propose a revised approach that leads to a better fit to a Gaussian distribution at no additional cost. Furthermore, the revised approach is physically better justified as the cloud effect is estimated by the maximum cloud effect of model and observations, which determines the largest possible departure. Preceding studies, in contrast, used the mean cloud effect of model and observations. Our study is based on a one-month data set of infrared observations in two water vapor channels (6.2 and 7.3 μm) from the Spinning Enhanced Visible and Infrared Imager (SEVIRI) onboard the Meteosat Second Generation satellite and corresponding simulations from the weather forecast model AROME (Application of Research to Operations at Mesoscale) over central Europe. The evaluation of different approaches revealed that near-Gaussian departures can be achieved with three different approaches for the estimation of the cloud effect: (1) deviation from a climatologically estimated value; (2) deviation from the clear-sky brightness temperature of a window channel; and (3) deviation from the clear-sky brightness temperature of the channel that is assimilated. For the lower-peaking channel (7.3 μm) with a larger cloud effect, best results were achieved with the first two options. For the 6.2 μm channel, the third option led to a slightly more Gaussian distribution. The third option, however, requires a quality control that eliminates about 10 % of the observations.

The sensitivity of all-sky infrared radiances to both hydrometeor content and cloud height leads to a very non-Gaussian distribution of first-guess departures. This non-Gaussianity can be mitigated by the application of cloud-dependent error models that normalize departures by an estimate of the cloud-height effect via assigning increased errors in situations with high clouds that can lead to very large departures. In the current study, we systematically evaluate existing error models and propose a revised approach that leads to a better fit to a Gaussian distribution at no additional cost. Furthermore, the revised approach is physically better justified as the cloud effect is estimated by the maximum cloud effect of model and observations, which determines the largest possible departure. Preceding studies, in contrast, used the mean cloud effect of model and observations. Our study is based on a one-month data set of infrared observations in two water vapor channels (6.2 and 7.3 μm) from the Spinning Enhanced Visible and Infrared Imager (SEVIRI) onboard the Meteosat Second Generation satellite and corresponding simulations from the weather forecast model AROME (Application of Research to Operations at Mesoscale) over central Europe. The evaluation of different approaches revealed that near-Gaussian departures can be achieved with three different approaches for the estimation of the cloud effect: (1) deviation from a climatologically estimated value; (2) deviation from the clear-sky brightness temperature of a window channel; and (3) deviation from the clear-sky brightness temperature of the channel that is assimilated. For the lower-peaking channel (7.3 μm) with a larger cloud effect, best results were achieved with the first two options. For the 6.2 μm channel, the third option led to a slightly more Gaussian distribution. The third option, however, requires a quality control that eliminates about 10 % of the observations.

Cloud-affected infrared satellites constitute a promising data source for numerical weather prediction models as they contain crucial information on atmospheric clouds and convective activity. Their sensitivity to both hydrometeor content and cloud top height, however, leads to a very non-Gaussian distribution of first-guess (FG) departures, which violates a fundamental assumption of current data assimilation schemes. To mitigate this issue, various cloud-dependent error models for normalizing the departures have been proposed (Geer and Bauer, 2011; Harnisch et al., 2016; Okamoto et al., 2014). In the current presentation, we revisit these error models and propose a refined approach that leads to a more Gaussian distribution.We quantify the performance of these error methods in detail when applied to one-month infrared observations from the Spinning Enhanced Visible and Infrared Imager (SEVIRI) onboard the Meteosat Second Generation and simulations from the weather forecast model AROME (Application of Research to Operations at Mesoscale) over Austria. While all methods can successfully yield approximately Gaussian FG departure distributions when normalized by the observation error, an additional quality control and a minimum error threshold is necessary for some of them.While previously published methods estimate the observation error using the average cloud effects from the model and observation spaces, we also introduce a new method that uses the maximum values from these two spaces for observation error calculation. Results show that the new method systematically outperforms the previous methods at no additional cost. Lastly, we analyze the performance of different practical implementation choices, such as using a linear or polynomial fit.Reference:Geer, A. J. and Bauer, P.: Observation errors in all-sky data assimilation, Quarterly Journal of the Royal Meteorological Society, 137, 2024–2037, https://doi.org/10.1002/qj.830, 2011.Harnisch, F., Weissmann, M., and Periáñez, Á.: Error model for the assimilation of cloud-affected infrared satellite observations in an ensemble data assimilation system, Quarterly Journal of the Royal Meteorological Society, 142, 1797–1808, https://doi.org/10.1002/qj.2776, 2016.Okamoto, K., McNally, A., and Bell, W.: Progress towards the assimilation of all-sky infrared radiances: An evaluation of cloud effects, Quarterly Journal of the Royal Meteorological Society, 140, 1603–1614, https://doi.org/10.1002/qj.2242, 2014.

The infrared satellite data have become an important source of assimilated data in numerical weather prediction (NWP) models. With the self-constructed assimilated module in the Weather Research and Forecasting model’s Data Assimilation (WRFDA) system, a set of cycling assimilation experiments is conducted to evaluate the added value of assimilating the Fengyun-4B (FY-4B) Advanced Geostationary Radiation Imager (AGRI) water vapor channels clear-sky data on analyses and forecasts for “23 · 7” heavy rainfall. The results show a notable reduction (50~60%) in the root mean square error (RMSE) of observed and simulated brightness temperature after assimilating AGRI and the positive analysis increments in temperature and humidity fields, which are conducive to precipitation formation. Furthermore, changes in humidity analysis caused by AGRI assimilation propagate from the upper to lower levels with assimilation cycling. Compared to the benchmark experiment, the AGRI assimilation experiments produce higher humidity conditions and more pronounced ascending motion, resulting in more realistic rainfall predictions at both location and intensity with higher rainfall scores, especially with the two-step assimilation scheme. Moreover, based on the results from sensitivity experiments, it is proven that the addition of a new channel 11 can further improve humidity and enhance rainfall location and intensity predictions. Overall, the clear-sky assimilation of the FY-4B AGRI water vapor channel data brings notable improvements to “23 · 7” heavy rainfall prediction.