The Atmospheric Infrared Sounder (AIRS), the hyperspectral infrared sounder on the NASA Aqua mission, both improves operational weather prediction and provides high-quality research data for climate studies. The Atmospheric Infrared Sounder (AIRS), and its two companion microwave instruments, the Advanced Microwave Sounding Unit (AMSU) and the Humidity Sounder for Brazil (HSB), form the integrated atmospheric sounding system flying on the Earth Observing System (EOS) Aqua spacecraft since its launch in May 2002.1 The primary scientific achievement of AIRS has been to improve weather prediction (Le Marshall et al. 2005a,b,c) and to study the water and energy cycle (Tian et al. 2006). AIRS also provides information on several greenhouse gases. The measurement goal of AIRS is the retrieval of temperature and precipitable-water vapor profiles with accuracies approaching those of conventional radiosondes. In the following text we use the terms AIRS and AIRS-AMSU-HSB interchangeably.1 The HSB ceased functioning after 5 February 2003. This did not have an impact on the accuracy, coverage, or resolution of the AIRS core data product, but its loss has had a significant impact on AIRS research products.A comprehensive set of articles on AIRS and AMSU design details, prelaunch calibration, and prelaunch retrieval performance expectations were published in a special issue of IEEE Transactions on Geoscience and Remote Sensing (2003, vol. 41, no. 2). This paper discusses the performance of AIRS and examines how it is meeting its operational and research objectives based on the experience of more than 2 yr with AIRS data. We describe the science background and the performance of AIRS in terms of the accuracy and stability of its observed spectral radiances. We examine the validation of the retrieved temperature and water vapor profiles against collocated operational radiosondes, and then we assess the impact thereof on numerical weather forecasting of the assimilation of the AIRS spectra and the retrieved temperature. We close the paper with a discussion on the retrieval of several minor tropospheric constituents from AIRS spectra.

Recognizing the importance and challenges inherent to the remote sensing of precipitation in mountainous areas, this study investigates the performance of the commonly used satellite-based high-resolution precipitation products (HRPPs) over several basins in the mountainous western United States. Five HRPPs [Tropical Rainfall Measuring Mission 3B42 and 3B42-RT algorithms, the Climate Prediction Center morphing technique (CMORPH), Precipitation Estimation from Remotely Sensed Imagery Using Artificial Neural Networks (PERSIANN), and the PERSIANN Cloud Classification System (PERSIANN-CCS)] are analyzed in the present work using ground gauge, gauge-adjusted radar, and CloudSat precipitation products. Using ground observation of precipitation and streamflow, the skill of HRPPs and the resulting streamflow simulations from the Variable Infiltration Capacity hydrological model are cross-compared. HRPPs often capture major precipitation events but seldom capture the observed magnitude of precipitation over the studied region and period . Bias adjustment is found to be effective in enhancing the HRPPs and resulting streamflow simulations. However, if not bias adjusted using gauges, errors are typically large as in the lower-level precipitation inputs to HRPPs. The results using collocated Advanced Microwave Scanning Radiometer for Earth Observing System (AMSR-E) and CloudSat precipitation data show that missing data, often over frozen land, and limitations in retrieving precipitation from systems that lack frozen hydrometeors contribute to the observed microwave-based precipitation errors transferred to HRPPs. Over frozen land, precipitation retrievals from infrared sensors and microwave sounders show some skill in capturing the observed precipitation climatology maps. However, infrared techniques often show poor detection skill, and microwave sounding in dry atmosphere remains challenging. By recognizing the sources of precipitation error and in light of the operation of the Global Precipitation Measurement mission, further opportunity for enhancing the current status of precipitation retrievals and the hydrology of cold and mountainous regions becomes available.

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The Western Land Data Assimilation System (WLDAS) is a custom instance of the NASA Land Information System that combines land surface parameters, meteorological forcing data, and satellite products within a land surface model to produce daily estimates of the water and energy budget variables for the western United States. WLDAS was configured through discussions with partners, with the goal of groundwater sustainability planning for the state of California in mind. The publicly available output dataset has a 1‐km grid resolution and spans 1979–present, which makes it suitable for water resources assessments. The data are also able to contextualize the recent drought events in California. Assimilation of Leaf Area Index, which is demonstrated herein to improve simulation over agricultural areas in California, specifically in terms of evapotranspiration in irrigation regions, will be included along with other data assimilation in subsequent releases of WLDAS.

[1] This study compares the atmospheric total precipitable water (PWV) obtained by Atmospheric Infrared Sounder (AIRS) with radiosondes and the European Centre for Medium-range Weather Forecasts (ECMWF) operational analysis products during December 2003 and January 2004. We find that PWV from AIRS Level 3 (daily gridded) data is about 9% drier while ECMWF is 14% moister than sondes at the two grid points closest to the Dome C radiosonde site on the Antarctic Plateau at 3233 m elevation. The largest ECMWF moist biases occur on warmer days at Dome C. When AIRS Level 3 data are compared with ECMWF over the entire Antarctic continent, AIRS and ECMWF PWV have similar variability (correlation coefficients are predominantly 0.8 or higher), but with AIRS drier over most of the Antarctic by a consistent offset of about 0.1-0.2 mm. Because of this constant difference, the largest percentage differences are found over the highland areas of about 2500 meters and above, where absolute water vapor amounts are smallest.