Strengths and weakness of remotely sensed winds are discussed, along with the current capabilities for remotely sensing winds and stress. Future missions are briefly mentioned. The observational needs for a wide range of wind and stress applications are provided. These needs strongly support a short list of desired capabilities of future missions and constellations.

The measurement of ocean surface wind speeds in precipitation from satellite microwave radiometers is a challenging task. Rain attenuates the signal that is emitted from the ocean surface.

In this study, observed cloud liquid water path (LWP) trends from the Multisensor Advanced Climatology of Liquid Water Path (MAC-LWP) dataset (1988–2014) are compared to trends computed from the temporally coincident records of 16 global climate models (GCMs) participating in phase 5 of the Coupled Model Intercomparison Project (CMIP5). For many regions, observed trend magnitudes are several times larger than the corresponding model mean trend magnitudes. Muted model mean trends are thought to be the result of cancellation effects arising from differing interannual variability characteristics and differences in model physics–dynamics. In most regions, the majority of modeled trends were statistically consistent with the observed trends. This was thought to be because of large estimated errors in both the observations and the models due to interannual variability. Over the southern oceans (south of 40°S latitude), general agreement between the observed trend and virtually all GCM trends is also found (about 1–2 g m−2 decade−1). Observed trends are also compared to those from the Atmospheric Model Intercomparison Project (AMIP). Like the CMIP5 models, the majority of modeled AMIP trends were statistically consistent with the observed trends. It was also found that, in regions where the AMIP model mean time series better captures observed interannual variability, it tends to better capture the magnitude of the observed trends.

Reliable sources for validating wind observations made by spaceborne microwave radiometer and scatterometer sensors above 15 m s−1 are scarce. Anemometers mounted on oil platforms provide usable wind speed measurements that can help fill this gap. In our study we compare wind speed observations from six microwave satellites (WindSat, AMSR-E, AMSR2, SMAP, QuikSCAT, and ASCAT) with wind speed records from 10 oil platform anemometers in the North and Norwegian Seas that were provided by the Norwegian Meteorological Institute. We study various forms of the vertical wind profile, which is required to convert anemometer winds to a reference height of 10 m above sea level. We create and analyze matchups between satellite and anemometer winds and find good agreement up to wind speeds of 30 m s−1 within the margin of errors. We also evaluate wind speeds from several analyses [ECMWF, NCEP, and Cross-Calibrated Multi-Platform (CCMP)]. We find them to be significantly lower than the anemometer winds with their biases increasing systematically with increasing wind speed. Important components of our analysis include a detailed discussion on the quality control of the anemometer winds and a quantitative analysis of the uncertainties in creating the matchups.

A new data set of tropical cyclone winds (‘TC-winds’) through rain as observed by the WindSat and AMSR2 microwave radiometers has been developed by making use of a linear combination of C- and X-band frequency channels. These winds, along with tropical cyclone winds from the SMAP L-band radiometer, are compared with the Hurricane Weather Research and Forecasting (HWRF) model. Due to differences in spatial scales between the satellites and the high-resolution HWRF model, resampling must be performed on the model winds before comparisons are done. Various ways of spatial resampling are discussed in detail, and an optimal method is determined. Additionally, resampled model winds must be temporally interpolated to the time of the satellite before direct comparisons are made. This interpolation can occasionally result in un-physical 2D wind fields, especially for fast-moving storms. To assist users with this problem, a methodology for handling un-physical wind features is detailed. Results of overall comparisons between the satellites and HWRF for 19 storms between 2017 and 2020 displayed consistent storm features, with overall average biases less than 1 m/s and standard deviations below 4 m/s for all tropical cyclone winds between 10 and 60 m/s. Differences were seen when the comparisons were performed separately for the Atlantic and Pacific basins, with biases and standard deviations between the satellites and HWRF showing better agreement in the Atlantic. The impact of rain on the satellite wind retrievals is discussed, and no systematic bias was seen between the three sensors, despite the fact that they use different frequency channels in their tropical cyclone winds-through-rain retrieval algorithms.