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.

The extension of all‐sky assimilation of Special Sensor Microwave Imager/Sounder (SSMIS) humidity‐sounding channels to land surfaces is investigated in this article. The scattering index, which is able to discriminate between cloudy and precipitating regions over land, is used as a predictor to develop a ‘symmetric’ model for observation error. This formulation is able to increase the observation error in those scenes that are more difficult to model because of radiative transfer and ‘mislocation’ errors. The implementation of an instantaneous emissivity retrieval from SSMIS observations is also presented. In clear‐sky scenes, emissivity retrievals appear better at capturing daily differences in surface conditions compared with emissivity atlas values. In the presence of clouds, retrievals show different behaviour. In the lower microwave frequencies (less than 50 GHz), emissivity estimates appear nearly as reliable as those in clear skies, but at higher frequencies, as the magnitude of scattering increases, so does the error in the retrieval and the resultant emissivity estimate can be unphysically low or high. However, the retrieval still appears feasible at high frequencies in light cloud situations; the number of retrievals discarded due to this kind of problem is around 10%. In these cases, an estimate from an emissivity atlas can be substituted instead. Together, the new observation‐error model and the instantaneous emissivity retrievals were adopted for the assimilation of SSMIS 183 GHz channels over land in all‐sky conditions. Assimilation experiments showed that the assimilation system is not degraded and the improvements in analysis and forecast scores are about the same as those obtained by the equivalent clear‐sky approach. The developments described in this study were an essential first step to create framework to allow the all‐sky assimilation over land of other microwave humidity sounders: this started operationally at the European Centre for Medium‐Range Weather Forecasts (ECMWF) in 2015, covering both SSMIS and four Microwave Humidity Sounder (MHS) instruments.

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Abstract. The general interest in the potential use of the mm and sub-mm frequencies up to 425 GHz resolution from geostationary orbit is increasing due to the fact that the frequent time sampling and the comparable spatial resolution relative to the "classical" (≤89 GHz) microwave frequencies would allow the monitoring of precipitating intense events for the assimilation of rain in now-casting weather prediction models.In this paper, we use the simulation of a heavy precipitating event in front of the coast of Crete island (Greece) performed by the University of Wisconsin -Non-hydrostatic Modeling System (UW-NMS) cloud resolving model in conjunction with a 3D-adjusted plane parallel radiative transfer model to simulate the upwelling brightness temperatures (TB's) at mm and sub-mm frequencies. To study the potential use of high frequencies, we first analyze the relationships of the simulated TB's with the microphysical properties of the UW-NMS simulated precipitating clouds, and then explore the capability of a Bayesian algorithm for the retrieval of surface rain rate, rain and ice water paths at such frequencies.

Abstract. Mesoscale cloud resolving models (CRM's) are often utilized to generate consistent descriptions of the microphysical structure of precipitating clouds, which are then used by physically-based algorithms for retrieving precipitation from satellite-borne microwave radiometers. However, in principle, the simulated upwelling brightness temperatures (TB's) and derived precipitation retrievals generated by means of different CRM's with different microphysical assumptions, may be significantly different even when the models simulate well the storm dynamical and rainfall characteristics. In this paper, we investigate this issue for two well-known models having different treatment of the bulk microphysics, i.e. the UW-NMS and the MM5. To this end, the models are used to simulate the same 24-26 November 2002 flood-producing storm over northern Italy. The model outputs that best reproduce the structure of the storm, as it was observed by the Advanced Microwave Scanning Radiometer (AMSR) onboard the EOS-Aqua satellite, have been used in order to compute the upwelling TB's. Then, these TB's have been utilized for retrieving the precipitation fields from the AMSR observations. Finally, these results are compared in order to provide an indication of the CRM-effect on precipitation retrieval.