A new coupled global NCEP Reanalysis for the period 1979-present is now available, at much higher temporal and spatial resolution, for climate studies. T he first reanalysis at NCEP (all acronyms are defined in the appendix), conducted in the 1990s, resulted in the NCEP-NCAR reanalysis (Kalnay et al. 1996), or R1 for brevity, and ultimately covered many years, from 1948 to the present (Kistler et al. 2001). It is still being executed at NCEP, to the benefit of countless users for monthly, and even daily, updates of the current state of the atmosphere. At the same time, other reanalyses were being conducted, namely, ERA-15 (Gibson et al. 1997) was executed for a more limited period (1979-93) at the ECMWF, COLA conducted a short reanalysis covering the May 1982-November 1983 period (Paolino et al. 1995), and NASA GSFC conducted a reanalysis covering the 1980-94 period (Schubert et al. 1997). The general purpose of conducting reanalyses is to produce multiyear global state-of-the-art gridded representations of atmospheric states, generated by a constant model and a constant data assimilation system. To use the same model and data assimilation over a very long period was the great advance during the 1990s, because gridded datasets available before 1995 had been created in real time by ever-changing models and analysis methods, even by hand analyses prior to about 1965. The hope was that a reanalysis,

The COSMIC radio occultation mission represents a revolution in atmospheric sounding from space, with precise, accurate, and all-weather global observations useful for weather, climate, and space weather research and operations. GPS Signal GPS Satellite

The Constellation Observing System for Meteorology, Ionosphere, and Climate (COSMIC) mission launched six small satellites in April 2006, each carrying a GPS radio occultation (RO) receiver. At final orbit, COSMIC will provide ∼2500–3000 RO soundings per day uniformly distributed around the globe in near–real time. In preparation for the assimilation of COSMIC data in an operational framework, the NCEP/Environmental Modeling Center (EMC) has successfully developed the capability of assimilating profiles of refractivity and bending angle. Each forward operator has been implemented with its own quality control and error characterization. In this paper, the infrastructure developed at NCEP/EMC to assimilate GPS RO observations, including forward models, observational and representativeness errors, and quality control procedures, is described. The advantages of using a forward operator for bending angle versus refractivity are discussed and some preliminary results on the benefits of the GPS RO in weather analysis and forecasts are presented. The different strategies adopted at NCEP/EMC to assimilate GPS RO data are aimed to select the most appropriate forward operator in the operational data assimilation system when COSMIC products are stable and routinely available to the Numerical Weather Centers. In the meantime, data from the Challenging Minisatellite Payload (CHAMP) satellite is available in non–real time and has been used in the assimilation tests to examine the potential benefits of the GPS RO–derived products. In the preliminary results presented in this study, the use of GPS RO observations slightly improves anomaly correlation scores for temperature (by ∼0.01–0.03) in the Southern Hemisphere and Tropics throughout the depth of the atmosphere while a slight degradation is found in the upper troposphere and stratosphere in the Northern Hemisphere. However, significant reduction of the temperature and humidity biases is found for all latitudes. The benefits from assimilating GPS RO data also extend to other fields, such as 500-hPa geopotential heights and tropical winds, demonstrating the potential use of GPS RO data in operational forecasting.

[1] Applications for space-based GPS technology have extended to the atmospheric field during the last two decades. More recently, numerical weather prediction (NWP) centers started incorporating global positioning system (GPS) radio occultation (RO) soundings into their operational assimilation algorithms, resulting in a significant improvement in weather forecasting skill. The main reasons for such benefits are the unbiased nature of the GPS RO measurements, high accuracy and precision, all-weather capability, and equal accuracy over either land or ocean. Profiles of refractivity or bending angle are typically used, owing to the relative simplicity of their forward operators and the computational economy this implies. Although the NOAA National Centers for Environmental Prediction (NCEP) are using refractivities in their operational configuration, a bending angle forward operator has been developed, tested, and implemented and was scheduled to replace the assimilation of refractivities operationally in May, 2012. Results from the NCEP's bending angle method (NBAM) show improvement over the assimilation of refractivities in all atmospheric fields being evaluated. A detailed description and evaluation of NBAM is presented in this article, as well as the advantages this code offers over the assimilation of refractivities and other existing algorithms that assimilate GPS RO bending angles.Citation: Cucurull, L., J. C. Derber, and R. J. Purser (2013), A bending angle forward operator for global positioning system radio occultation measurements,

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Satellite radiance measurements are used daily at numerical weather prediction (NWP) centers around the world, providing a significant positive impact on weather forecast skill. Owing to the existence of systematic errors, either in the observations, instruments, and/or forward models, which can be larger than the signal, the use of infrared or microwave radiances in data assimilation systems requires significant bias corrections. As most bias-correction schemes do not correct for biases that exist in the model forecasts, the model needs to be grounded by an unbiased observing system. These reference measurements, also known as ''anchor observations,'' prevent a drift of the model to its own climatology and associated biases, thus avoiding a spurious drift of the observation bias corrections. This paper shows that the assimilation of global positioning system (GPS) radio occultation (RO) observations over a 3-month period in an operational NWP system results in smaller, more accurate bias corrections in infrared and microwave observations, resulting in an overall more effective use of satellite radiances and a larger number of radiance observations that pass quality control. A full version of the NCEP data assimilation system is used to evaluate the results on the bias corrections for the High Resolution Infrared Radiation Sounder-3 (HIRS-3) on NOAA-17 and the Advanced Microwave Sounding Unit-A (AMSU-A) on NOAA-15 in an operational environment.

Abstract. We combine GPS/MET data from 29 occultations and IGS ground data collected from 160 stations around the world to perform stochastic tomography of the ionosphere with a 4120 120 global grid of voxels extending from 200 to 650 km above the mean surface of the Earth. A correlation functional approach that limits the spatial high frequency content of the images is used, and a Kalman filter is applied in the time direction. The combination of ground and occultation data and the use of smoothing techniques is robust enough for vertical resolution in this four-layer model analysis. We discuss the role of noise on the choice of the correct range of eigenvalues in the inversion problem, and the impact of occultation data, showing that ground data alone is insufficient for vertical resolution even in a three-layer, noise-less simulation.