Ten years ago, humidity observations were thought to give little benefit to global weather forecasts. Nowadays, at the European Centre for Medium‐range Weather Forecasts, satellite microwave radiances sensitive to humidity, cloud and precipitation provide 20% of short‐range forecast impact, as measured by adjoint‐based forecast sensitivity diagnostics. This makes them one of the most important sources of data and equivalent in impact to microwave temperature sounding observations. Forecasts of dynamical quantities, and precipitation, are improved out to at least day 6. This article reviews the impact of and the science behind these data. It is not straightforward to assimilate cloud and precipitation‐affected observations when the intrinsic predictability of cloud and precipitation features is limited. Assimilation systems must be able to operate in the presence of all‐pervasive cloud and precipitation ‘mislocation’ errors. However, by assimilating these observations using the ‘all‐sky’ approach, and supported by advances in data assimilation and forecast modelling, modern data assimilation systems can infer the dynamical state of the atmosphere, not just from traditional temperature‐related observations, but from observations of humidity, cloud and precipitation.

Abstract. To simulate passive microwave radiances in allsky conditions requires better knowledge of the scattering properties of frozen hydrometeors. Typically, snow particles are represented as spheres and their scattering properties are calculated using Mie theory, but this is unrealistic and, particularly in deep-convective areas, it produces too much scattering in mid-frequencies (e.g. 30-50 GHz) and too little scattering at high frequencies (e.g. 150-183 GHz). These problems make it hard to assimilate microwave observations in numerical weather prediction (NWP) models, particularly in situations where scattering effects are most important, such as over land surfaces or in moisture sounding channels. Using the discrete dipole approximation to compute scattering properties, more accurate results can be generated by modelling frozen particles as ice rosettes or simplified snowflakes, though hexagonal plates and columns often give worse results than Mie spheres. To objectively decide on the best particle shape (and size distribution) this study uses global forecast departures from an NWP system (e.g. observation minus forecast differences) to indicate the quality of agreement between model and observations. It is easy to improve results in one situation but worsen them in others, so a rigorous method is needed: four different statistics are checked; these statistics are required to stay the same or improve in all channels between 10 GHz and 183 GHz and in all weather situations globally. The optimal choice of snow particle shape and size distribution is better across all frequencies and all weather conditions, giving confidence in its physical realism. Compared to the Mie sphere, most of the systematic error is removed and departure statistics are improved by 10 to 60 %. However, this improvement is achieved with a simple "one-size-fits-all" shape for snow; there is little additional benefit in choosing the particle shape according to the precipitation type. These developments have improved the accuracy of scattering radiative transfer sufficiently that microwave all-sky assimilation is being extended to land surfaces, to higher frequencies and to sounding channels.

Abstract. To simulate passive microwave radiances in all-sky conditions requires better knowledge of the scattering properties of frozen hydrometeors. Typically, snow particles are represented as spheres and their scattering properties are calculated using Mie theory, but this is unrealistic and particularly in deep-convective areas, it produces too much scattering in mid frequencies (e.g. 30–50 GHz) and too little scattering at high frequencies (e.g. 150–183 GHz). These problems make it hard to assimilate microwave observations in numerical weather prediction (NWP) models, particularly in situations where scattering effects are most important such as over land surfaces or in moisture sounding channels. Using the discrete dipole approximation to compute scattering properties, more accurate results can be generated by modelling frozen particles as ice rosettes or simplified snowflakes, though hexagonal plates and columns often give worse results than Mie spheres. To objectively decide on the best particle shape (and size distribution) this study uses global forecast departures from an NWP system (e.g. observation minus forecast differences) to indicate the quality of agreement between model and observations. It is easy to improve results in one situation but worsen them in others, so a rigorous method is needed: four different statistics are checked; these statistics are required to stay the same or improve in all channels between 10 GHz and 183 GHz and in all weather situations globally. The optimal choice of snow particle shape and size distribution is better across all frequencies and all weather conditions, giving confidence in its physical realism. Compared to the Mie sphere, most of the systematic error is removed and departure statistics are improved by 10 to 60%. However, this improvement is achieved with a simple “one-size-fits-all” shape for snow; there is little additional benefit in choosing the particle shape according to the precipitation type. These developments have improved the accuracy of scattering radiative transfer sufficiently that microwave all-sky assimilation is being extended to land surfaces, to higher frequencies and to sounding channels.

Ground‐based measurements of stratospheric constituents were carried out from Thule Air Base, Greenland (76.5°N, 68.7°W), during the winters of 2001–2002 and 2002–2003, involving operation of a millimeter‐wave spectrometer (GBMS) and a lidar system. This work focuses on the GBMS retrievals of stratospheric O3, CO, N2O, and HNO3, and on lidar stratospheric temperature data obtained during the first of the two winter campaigns, from mid‐January to early March 2002. For the Arctic lower stratosphere, the winter 2001–2002 is one of the warmest winters on record. During a large fraction of the winter, the vortex was weakened by the influence of the Aleutian high, with low ozone concentrations and high temperatures observed by GBMS and lidar above ∼27 km during the second half of February and in early March. At 900 K (∼32 km altitude), the low ozone concentrations observed by GBMS in the Aleutian high are shown to be well correlated to low solar exposure. Throughout the winter, PSCs were rarely observed by POAM III, and the last detection was recorded on 17 January. During the lidar and GBMS observing period that followed, stratospheric temperatures remained above the threshold for PSCs formation throughout the vortex. Nonetheless, using correlations between GBMS O3 and N2O mixing ratios, in early February a large ozone deficiency owing to local ozone loss is noted inside the vortex. GBMS O3‐N2O correlations suggest that isentropic transport brought a O3 deficit also to regions near the vortex edge, where transport most likely mimicked local ozone loss.