Implementation and validation of a new weather filter for reducing weather effect in the ASMR2 sea ice concentration data

JAXA has developed a global snow cover dataset from satellites covering 1978 to the present. New and earlier observations have been combined to continue the record. Ground data show over 80% snow-detection accuracy, while that of wet-snow detection is about 60–80%. The new data closely match the previous record. Analysis shows that Northern Hemisphere snow cover extent has decreased in all seasons, particularly in western Eurasia, although changes vary by region, highlighting warming’s effects.

We developed an eddy-permitting local ensemble Kalman filter (LETKF)-based Ocean Research Analysis (LORA) version 2.0 for a quasi-global domain (LORA-QG) covering the period from June 2002 onward. Validation shows that LORA-QG achieves accuracy comparable to existing global ocean reanalysis datasets. LORA-QG includes ensemble-based uncertainty estimates and individual terms of the heat and salinity budget equations, making it a valuable resource for ocean forecasting and process-based studies.

In a warmer future, water vapour will spend more time in the atmosphere, changing global rainfall patterns. In this study, we analysed the performance of 28 water vapour records between 1988 and 2014. We find sensitivity to surface warming generally outside expected ranges, attributed to breakpoints in individual record trends and differing representations of climate variability. The implication is that longer records are required for high confidence in assessing climate trends.

An adaptive observation error inflation (AOEI) method was proposed for atmospheric data assimilation to mitigate erroneous analysis updates caused by large observation-minus-forecast differences for satellite brightness temperature around clear- and cloudy-sky boundaries. This study implemented the AOEI with an ocean data assimilation system, leading to an improvement of analysis accuracy and dynamical balance around the frontal regions with large meridional temperature differences.

We develop an ensemble-Kalman-filter-based regional ocean data assimilation system in which satellite and in situ observations are assimilated at a daily frequency. We find the best setting for dynamical balance and accuracy based on sensitivity experiments focused on how to inflate the ensemble spread and how to apply the analysis update to the model evolution. This study has a broader impact on more general data assimilation systems in which the initial shocks are a significant issue.