An Assessment of Southern Hemisphere Extratropical Cyclones in ERA5 Using WindSat

ERA5 reanalysis output is compared to WindSat polarimetric microwave radiometer measurements for Southern Hemisphere midlatitude to high‐latitude cyclones between 2003 and 2019. WindSat provides independent measures of low‐level wind speed, total column water vapor (TCWV), cloud liquid water (CLW), and precipitation, which are not assimilated into ERA5. We implement a tracking scheme to identify cyclone centers, before using cyclone composites to match concurrent data in ERA5 and WindSat. We find ERA5 and WindSat show comparable spatial structures for all variables, although their distributions show poorer agreement for CLW and precipitation. Compared to WindSat, ERA5 underestimates TCWV by up to 5% and CLW by up to 40%. ERA5 underestimates precipitation in the warm sector by up to 15%, but overestimates in the cold sector by up to 60%. Similar biases in ERA5 are seen compared to Advanced Microwave Scanning Radiometer for EOS (AMSR‐E) data, even though AMSR‐E radiances are assimilated into ERA5. Comparing ERA5 and WindSat across the cyclone lifecycle, strong spatial correlation is seen as the cyclone deepens and reaches peak intensity, before slightly declining as the cyclone decays. In the cold sector ERA5 shows an underestimation of CLW, yet overestimates precipitation at all lifecycle stages. However, in the warm sector precipitation is underestimated. This potentially suggests biases within the ERA5 parameterizations of cloud and precipitation causing a disconnect between the two. Despite this, ERA5 shows strong correlation with WindSat and determines cyclone structure well across the cyclone lifecycle, showing its value for use in cyclone compositing analysis. Plain Language Summary Extratropical cyclones play a major role in the circulation within the atmosphere which acts to transfer heat toward the poles. Here we assess the representation of extratropical cyclones within the ERA5 reanalysis by comparing with observations made by the WindSat satellite. Because WindSat data are not used as input to the ERA5 model, it provides an independent measure of the quality of ERA5. By tracking low pressure cyclone centers, we can identify a set of cyclones which can then be used to determine the average behavior of a cyclone. We find that both ERA5 and WindSat show similar features across the cyclone for near surface wind speed, water vapor, cloud liquid water (CLW) and rainfall. However, ERA5 shows discrepancies with WindSat with underestimates of CLW and ove