Toward All-Sky Assimilation of Microwave Temperature Sounding Channels in Environment Canada’s Global Deterministic Weather Prediction System

Abstract

The all-sky assimilation of radiances from microwave instruments is developed in the 4D-EnVar analysis system at Environment and Climate Change Canada (ECCC). Assimilation of cloud-affected radiances from Advanced Microwave Sounding Unit A (AMSUA) temperature sounding channels 4 and 5 for non-precipitating scenes over the ocean surface is the focus of this study. Cloud-affected radiances are discarded in the ECCC operational data assimilation system due to the limitations of forecast model physics, radiative transfer models, and the strong non-linearity of the observation operator. In addition to using symmetric estimate of innovation standard deviation for quality control, a state-dependent observation error inflation is employed at the analysis stage. The background state clouds are scaled by a factor of 0.5 to compensate for a systematic overestimation by the forecast model, before being used in the observation operator. The changes in the fit of the background state to observations show mixed results. The number of AMSUA channels 4 and 5 assimilated observations in the all-sky experiment is 5-12% higher than in the operational system. The all-sky approach improves temperature analysis when verified against ECMWF operational analysis in the areas where the extra cloud-affected observations were assimilated. Statistically significant reductions in error standard deviation by 1-4% for the analysis and forecasts of temperature, specific humidity, and horizontal wind speed up to maximum 4 days were achieved in the all-sky experiment in the lower troposphere. These improvements result mainly from the use of cloud information for computing the observation-minus-background departures. The operational implementation of all-sky assimilation is planned for Fall 2021.

Recent advancements for tropical cyclone data assimilation

“…All‐sky radiance assimilation has been shown to contribute greatly to improved forecasts of dynamic quantities and precipitation beyond day 6 119 . Other operational centers, such as NCEP, 120–122 Met Office, 123 JMA, 124 and Environment and Climate Change Canada, 125 have also devoted significant efforts to expand the radiance assimilation beyond only clear‐sky conditions. Global improvements to the temperature, humidity, and wind fields from the all‐sky radiance assimilation could directly benefit TC analyses and prediction through the assimilation of the additional radiance information within the cloudy and precipitating areas.…”

“…48,118 All-sky radiance assimilation has been shown to contribute greatly to improved forecasts of dynamic quantities and precipitation beyond day 6. 119 Other operational centers, such as NCEP, [120][121][122] Met Office, 123 JMA, 124 and Environment and Climate Change Canada, 125 have also devoted significant efforts to expand the radiance assimilation beyond only clear-sky conditions.…”

A Machine Learning Augmented Data Assimilation Method for High‐Resolution Observations

“…Remotely sensed observations are integral to the performance of numerical weather forecasts, especially since in‐situ observations of large portions of the atmosphere and surface are sparse (Bannister, 2017; Cardinali, 2009). Efforts to incorporate satellite and other remotely sensed observations into assimilation systems have been effective at improving model initialization and forecast accuracy (Buehner et al., 2018; Shahabadi & Buehner, 2021). However, as a result of the expense associated with assimilation much of the potential of these high‐resolution measurements for improving state initialization in forecast models has not been realized.…”

“…For others, particularly for remotely sensed data such as high‐resolution satellite measurements, this calculation itself is a physics‐based model that can be a computational bottleneck (Eyre et al., 2022). In some operational models, the trade‐off between the speed and accuracy of these observation operators is already an important avenue of research for improving the performance of their DA systems even before currently unusable high‐resolution data is considered (Shahabadi & Buehner, 2021). The observation operator calculation must be performed for each data point and so also scales with the number of discrete observations, again increasing its cost.…”

The Potential Value of Combining Assimilation of Water Vapor Radiances and Cloud Liquid/Ice Water Path From ABI on Analyses and Forecasts for Hurricane Irma (2017)

“…Beyond the typical difficulties shared by the direct assimilation of radiances, such as large uncertainties and biases in the Radiative Transfer Model for cloud absorption and scattering (Li et al., 2016; Okamoto, 2017), the inconsistency between the simulated hydrometeors and observations (Barlakas et al., 2021), and high nonlinearity for the cloudy radiance (Errico et al., 2007), direct assimilation of cloud‐affected IR, near‐IR, and VIS radiances suffers from the significant challenge of the high sensitivity of Jacobian to clouds (Li et al., 2016; Vidot et al., 2015). In addition, most of the channels currently used for IR imager all‐sky assimilation are water vapor channels, making the thermodynamic information updated but not the cloud due to the lack of direct observations (e.g., Candy & Migliorini, 2021; Geer et al., 2019; Lee et al., 2020; Shahabadi & Buehner, 2021; Xian et al., 2019; Zhang et al., 2016).…”