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Generally, only clear-infrared spectral radiances (not affected by clouds) are assimilated in weather analysis systems. This is due to difficulties in modeling cloudy radiances as well as in observing their vertical structure from space. To take full advantage of the thermodynamic information in advanced infrared (IR) sounder observations requires assimilating radiances from cloud-contaminated regions. An optimal imager/sounder cloud-clearing technique has been developed by the Cooperative Institute for Meteorological Satellite Studies at the University of Wisconsin-Madison. This technique can be used to retrieve clear column radiances through combining collocated multiband imager IR clear radiances and the sounder cloudy radiances; no background information is needed in this method. The imager/sounder cloud-clearing technique is similar to that of the microwave/IR cloud clearing in the derivation of the clear-sky equivalent radiances. However, it retains the original IR sounder resolution, which is critical for regional numerical weather prediction applications. In this study, we have investigated the assimilation of cloud-cleared IR sounder radiances using Atmospheric Infrared Sounder (AIRS)/Moderate Resolution Imaging Spectroradiometer for three hurricanes, Sandy (2012), Irene (2011), and Ike (2008). Results show that assimilating additional cloud-cleared AIRS radiances reduces the 48 and 72 h temperature forecast root-mean-square error by 0.1–0.3 K between 300 and 850 hPa. Substantial improvement in reducing track forecasts errors in the range of 10 km to 50 km was achieved.

This paper presents the retrieval algorithm of nitrous oxide (N2O) using the Atmospheric Infrared Sounder (AIRS) on EOS/Aqua, its validation using aircraft measurements, and one possible application for monitoring the global N2O annual trend from 2003 to 2013. The results demonstrate that AIRS is sensitive to N2O in the middle to upper troposphere, with the peak vertical sensitivity between 200 and 750 hPa and the sensitivity in the tropics larger than in the high-latitude regions. The degrees of freedom of the N2O retrieval are mostly between 1.0 and 1.5. Validation using the aircraft measurement profiles by the High-Performance Instrumented Airborne Platform for Environmental Research Pole-to-Pole Observations program over the Pacific Ocean indicated that the retrieval RMS error is mostly less than 8 ppb (or ~2.1%). One important feature is that the variability of N2O from AIRS is more than 2 times than that of the aircraft measurements in the lower troposphere. In agreement with surface measurements, a nearly linear trend of N2O can be obtained based on limited AIRS data of 1 day in 15 May in each year from 2003 to 2013, and the increase rate of N2O is about 0.72 ppb yr−1. This algorithm will be implemented in AIRS operational retrieval system, enabling the derivation of the N2O for over 20 years using the AIRS, the Infrared Atmospheric Sounding Interferometer, and the Cross-track Infrared Sounder. Such a unique product will be complementary to currently sparse ground-based observations for monitoring the N2O trend associated with climate change.

The microwave sounding unit (MSU) prelaunch thermal-vacuum chamber test data for eight MSU flight models that flew on TIROS-N, NOAA 7 through NOAA 12, and NOAA 14 were reanalyzed using an improved calibration algorithm, originally designed for the advanced microwave sounding unit-A (AMSU-A) operations. The new calibration algorithm can automatically adjust for any channel gain shift in operation. Adoption of this calibration algorithm as the MSU calibration procedure in the recalibration project will make the data sets from the MSU and the AMSU-A more consistent. This will be useful for future blending of the climate trends generated from the MSU and AMSU-A data. A single nonlinearity parameter u, which appears in the new calibration algorithm, was obtained for each channel from analysis of the prelaunch calibration test data. A software package for implementing this new calibration algorithm was developed and applied to calculate the MSU time series for improvement of the accuracy of the climate record. Sample calculations of MSU antenna temperatures with the new calibration algorithm were made for two satellites and are compared with similar results obtained from the old MSU calibration algorithm. Significant differences are observed, and strong evidence indicates that the new calibration procedure will provide a more accurate quantification of climate trends.

[1] Infrared (IR) land surface emissivity (LSE) with a high temporal and spatial resolution is very important for deriving other products using IR radiance measurements as well as assimilating IR radiances in numerical weather prediction (NWP) models over land. Retrieved from various satellite instruments, many LSE databases are available for operational and research use. Most are updated only monthly; assuming emissivity does not change within the month. However, laboratory measurements have shown that emissivity increases by 1.7% to 16% when soil moisture content becomes higher, especially in sandy soils in the 8.2–9.2 μm range. And a clearly defined wave-like diurnal pattern of decreasing surface soil moisture during the day and recovery (or increased soil moisture) at night was observed. Therefore, it is expected that LSE possesses a diurnal wave-pattern variation with low values during day time and high values during nighttime. The physically based GOES-R ABI LSE algorithm uniquely exploits the geostationary satellites' high temporal resolution. The algorithm was developed and applied to the radiance measurements from the Spinning Enhanced Visible and InfraRed Imager (SEVIRI) on the Meteosat Second Generation (MSG) Meteosat-8/9. The results over the Sahara Desert show that 8.7μm emissivity has a significant diurnal wave-pattern variation, with high values during nighttime and low values during day time. 10.8μm emissivity also shows a similar diurnal variation, but with a smaller amplitude compared to 8.7 μm. 12.0 μm emissivity has an even weaker diurnal variation, and an opposite pattern as 8.7 and 10.8 μm. Evidence is provided to demonstrate that the SEVIRI LSE diurnal wave-pattern variations are real, not artifacts from the retrieval algorithm. The impacts of diurnal variations of errors in GFS forecast (temperature and moisture profiles) and in land surface temperature (LST) are analyzed; they are found to be minor compared to the LSE diurnal variations shown by SEVIRI.

[1] NOAA's next generation polar-orbiting environmental satellite system, designated as the Joint Polar Satellite System (JPSS), was proposed in February 2010, as part of the President's Fiscal Year 2011 budget request, to be the Civilian successor to the restructured National Polar-Orbiting Operational Environmental Satellite System (NPOESS). Beginning 1 October 2013, the JPSS baseline consists of a suite of five instruments: advanced microwave and infrared sounders critical for short- and medium-range weather forecasting; an advanced visible and infrared imager needed for environmental assessments such as snow/ice cover, droughts, volcanic ash, forest fires and surface temperature; ozone sensor primarily used for global monitoring of ozone and input to weather and climate models; and an Earth radiation budget sensor for monitoring the Earth's energy budget. NASA will fund the Earth radiation budget sensor and the ozone limb sensor for the second JPSS operational satellite—JPSS-2. JPSS is implemented through a partnership between NOAA and the U.S. National Aeronautics and Space Administration (NASA). NOAA is responsible for overall funding; maintaining the high-level requirements; establishing international and interagency partnerships; developing the science and algorithms, and user engagement; NOAA also provides product data distribution and archiving of JPSS data. NASA's role is to serve as acquisition Center of Excellence, providing acquisition of instruments, spacecraft and the multimission ground system, and early mission implementation through turnover to NOAA for operations.