Assimilation of surface‐sensitive bands' clear‐sky radiance data using retrieved surface temperatures from geostationary satellites

Abstract

Clear‐sky radiances (CSRs) derived from observations made by imager sensors on board geostationary satellites are widely used in most operational numerical weather prediction systems. CSRs have data on tropospheric water vapour and temperatures, and the products at water vapour bands are generally assimilated into global data assimilation systems. In another band, known as the CO2 band (13.3–13.4 μm), CSRs are not used widely yet, despite having a wealth of information about temperatures in the mid‐ and low troposphere. This is mainly because of the high surface sensitivity of this band, which makes it difficult to accurately simulate brightness temperatures when there are non‐negligible errors in the surface parameters in the models. This article quantitatively investigated the surface sensitivities of CO2 and water vapour bands, which have the sensitivity under dry atmospheric conditions, and developed retrieval of land surface temperature (LST) from window band CSRs to obtain a more accurate simulated brightness temperature. Additionally, it was discovered that the retrieved LST outperformed that from the model in short‐range forecasts for the low‐water‐vapour band (7.3 μm) CSR data assimilation, and throughout the forecasting period, especially in the tropics, for the CO2 band CSR data assimilation. We also examined an unexpected improvement in the low troposphere in the model's LST trials, and we concluded that it was related to the relationship between the LST and atmospheric temperature biases.

All‐sky infrared radiance assimilation of a geostationary satellite in the Japan Meteorological Agency's global system

“…Kazunori (2018) and Okabe and Okamoto (2023) have already presented the assimilation processing for CSR; therefore, a brief description will be given here. Hourly CSR observations were assimilated after thinning to 220 km resolution and cloud contamination screenings.…”

“…Observation error SDs were fixed (not situation dependent) to 1.5 K for all the water vapor bands, and inter-band observation error correlation was not considered. Observations were further excluded in conditions of relatively difficult simulations with satellite zenith angle exceeding 62.5 • and orography over 4,000 m. CSR assimilation used a land skin temperature (LST) retrieved from observations at the window band, leading to surface-sensitive bands that improve the background and short-range forecasts (Okabe and Okamoto, 2023). The land-surface emissivity adopted the UWIRemis atlas of RTTOV (Borbas and Ruston, 2010).…”