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Abstract Observations from microwave temperature sounders such as the Advanced Microwave Sounding Unit‐A (AMSU‐A) provide some of the largest contributions to forecast skill in numerical weather prediction. Currently, AMSU‐A radiances are assimilated under all‐sky conditions at operational centres without accounting explicitly for spatial observation‐error correlations. To mitigate the impact of unrepresented correlated errors, strategies like spatial thinning (reducing observation density) and inflated observation‐error variances are commonly used. Here we present new estimates of spatial observation‐error correlations for the AMSU‐A all‐sky systems from the European Centre for Medium‐Range Weather Forecasts (ECMWF) and the Met Office, using diagnostics from background and analysis departures. Results are presented for three different cases: when all data are considered and when data are separated by surface type and cloud cover. High spatial observation‐error correlations are seen particularly for tropospheric channels (4–8) over land, with correlation length‐scales ranging from 75 to 125 km. We hypothesise that these correlations originate primarily from inadequacies in the modelling of surface emissivity, surface skin temperature, clouds, or precipitation. Our findings suggest that an increase in forecast skill could be achieved by following a pragmatic strategy of increasing assimilated observation density for stratospheric channels (9–14) and all channels 4–14 over the ocean, due to the negligible error correlations in these situations. In contrast, for tropospheric channels over land, exploiting the available data fully through reduced thinning requires accounting for spatial observation‐error correlations.
Bhatt et al. (Thu,) studied this question.