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Yong Chen, Yong Han, Paul van Delst, and Fuzhong Weng

Abstract

The nadir-viewing satellite radiances at shortwave infrared channels from 3.5 to 4.6 μm are not currently assimilated in operational numerical weather prediction data assimilation systems and are not adequately corrected for applications of temperature retrieval at daytime. For satellite observations over the ocean during the daytime, the radiance in the surface-sensitive shortwave infrared is strongly affected by the reflected solar radiance, which can contribute as much as 20.0 K to the measured brightness temperatures (BT). The nonlocal thermodynamic equilibrium (NLTE) emission in the 4.3-μm CO2 band can add a further 10 K to the measured BT. In this study, a bidirectional reflectance distribution function (BRDF) is developed for the ocean surface and an NLTE radiance correction scheme is investigated for the hyperspectral sensors. Both effects are implemented in the Community Radiative Transfer Model (CRTM). The biases of CRTM simulations to Infrared Atmospheric Sounding Interferometer (IASI) observations and the standard deviations of the biases are greatly improved during daytime (about a 1.5-K bias for NLTE channels and a 0.3-K bias for surface-sensitive shortwave channels) and are very close to the values obtained during the night. These improved capabilities in CRTM allow for effective uses of satellite data at short infrared wavelengths in data assimilation systems and in atmospheric soundings throughout the day and night.

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Sunggi Chung, Steven Ackerman, Paul F. van Delst, and W. Paul Menzel

Abstract

This paper investigates the relationship between high–spectral resolution infrared (IR) radiances and the microphysical and macrophysical properties of cirrus clouds. Through use of radiosonde measurements of the atmospheric state at the Department of Energy’s Atmospheric Radiation Measurement Program site, high–spectral resolution IR radiances are calculated by combining trace gas absorption optical depths from a line-by-line radiative transfer model with the discrete ordinate radiative transfer (DISORT) method. The sensitivity of the high–spectral resolution IR radiances to particle size, ice-water path, cloud-top location, cloud thickness, and multilayered cloud conditions is estimated in a multitude of calculations.

DISORT calculations and interferometer measurements of cirrus ice cloud between 700 and 1300 cm−1 are compared for three different situations. The measurements were made with the High–Spectral Resolution Interferometer Sounder mounted on a National Aeronautics and Space Administration ER-2 aircraft flying at 20-km altitude during the Subsonic Aircraft Contrail and Cloud Effects Special Study (SUCCESS).

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Quanhua Liu, Xingming Liang, Yong Han, Paul van Delst, Yong Chen, Alexander Ignatov, and Fuzhong Weng

Abstract

The Community Radiative Transfer Model (CRTM) developed at the Joint Center for Satellite Data Assimilation (JCSDA) is used in conjunction with a daily sea surface temperature (SST) and the National Centers for Environmental Prediction (NCEP) Global Forecast System (GFS) atmospheric data and surface wind to calculate clear-sky top-of-atmosphere (TOA) brightness temperatures (BTs) in three Advanced Very High Resolution Radiometer (AVHRR) thermal infrared channels over global oceans. CRTM calculations are routinely performed by the sea surface temperature team for four AVHRR instruments on board the National Oceanic and Atmospheric Administration (NOAA) satellites NOAA-16, NOAA-17, and NOAA-18 and the Meteorological Operation (MetOp) satellite MetOp-A, and they are compared with clear-sky TOA BTs produced by the operational AVHRR Clear-Sky Processor for Oceans (ACSPO). It was observed that the model minus observation (M−O) bias in the NOAA-16 AVHRR channel 3b (Ch3b) centered at 3.7 μm experienced a discontinuity of ∼0.3 K when a new CRTM version 1.1 (v.1.1) was implemented in ACSPO processing in September 2008. No anomalies occurred in any other AVHRR channel or for any other platform. This study shows that this discontinuity is caused by the out-of-band response in NOAA-16 AVHRR Ch3b and by using a single layer to the NCEP GFS temperature profiles above 10 hPa for the alpha version of CRTM. The problem has been solved in CRTM v.1.1, which uses one of the six standard atmospheres to fill in the missing data above the top pressure level in the input NCEP GFS data. It is found that, because of the out-of-band response, the NOAA-16 AVHRR Ch3b has sensitivity to atmospheric temperature at high altitudes. This analysis also helped to resolve another anomaly in the absorption bands of the High Resolution Infrared Radiation Sounder (HIRS) sensor, whose radiances and Jacobians were affected to a much greater extent by this CRTM inconsistency.

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Yong Chen, Yong Han, Quanhua Liu, Paul Van Delst, and Fuzhong Weng

Abstract

To better use the Stratospheric Sounding Unit (SSU) data for reanalysis and climate studies, issues associated with the fast radiative transfer (RT) model for SSU have recently been revisited and the results have been implemented into the Community Radiative Transfer Model version 2. This study revealed that the spectral resolution for the sensor’s spectral response functions (SRFs) calculations is very important, especially for channel 3. A low spectral resolution SRF results, on average, in 0.6-K brightness temperature (BT) errors for that channel. The variations of the SRFs due to the CO2 cell pressure variations have been taken into account. The atmospheric transmittance coefficients of the fast RT model for the Television and Infrared Observation Satellite (TIROS)-N, NOAA-6, NOAA-7, NOAA-8, NOAA-9, NOAA-11, and NOAA-14 have been generated with CO2 and O3 as variable gases. It is shown that the BT difference between the fast RT model and line-by-line model is less than 0.1 K, but the fast RT model is at least two orders of magnitude faster. The SSU measurements agree well with the simulations that are based on the atmospheric profiles from the Earth Observing System Aura Microwave Limb Sounding product and the Sounding of the Atmosphere using Broadband Emission Radiometry on the Thermosphere Ionosphere Mesosphere Energetics and Dynamics satellite. The impact of the CO2 cell pressures shift for SSU has been evaluated by using the Committee on Space Research (COSPAR) International Reference Atmosphere (CIRA) model profiles. It is shown that the impacts can be on an order of 1 K, especially for SSU NOAA-7 channel 2. There are large brightness temperature gaps between observation and model simulation using the available cell pressures for NOAA-7 channel 2 after June 1983. Linear fittings of this channel’s cell pressures based on previous cell leaking behaviors have been studied, and results show that the new cell pressures are reasonable. The improved SSU fast model can be applied for reanalysis of the observations. It can also be used to address two important corrections in deriving trends from SSU measurements: CO2 cell leaking correction and atmospheric CO2 concentration correction.

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Yanqiu Zhu, Emily Liu, Rahul Mahajan, Catherine Thomas, David Groff, Paul Van Delst, Andrew Collard, Daryl Kleist, Russ Treadon, and John C. Derber

Abstract

The capability of all-sky microwave radiance assimilation in the Gridpoint Statistical Interpolation (GSI) analysis system has been developed at the National Centers for Environmental Prediction (NCEP). This development effort required the adaptation of quality control, observation error assignment, bias correction, and background error covariance to all-sky conditions within the ensemble–variational (EnVar) framework. The assimilation of cloudy radiances from the Advanced Microwave Sounding Unit-A (AMSU-A) microwave radiometer for ocean fields of view (FOVs) is the primary emphasis of this study.

In the original operational hybrid 3D EnVar Global Forecast System (GFS), the clear-sky approach for radiance data assimilation is applied. Changes to data thinning and quality control have allowed all-sky satellite radiances to be assimilated in the GSI. Along with the symmetric observation error assignment, additional situation-dependent observation error inflation is employed for all-sky conditions. Moreover, in addition to the current radiance bias correction, a new bias correction strategy has been applied to all-sky radiances. In this work, the static background error variance and the ensemble spread of cloud water are examined, and the levels of cloud variability from the ensemble forecast in single- and dual-resolution configurations are discussed. Overall, the all-sky approach provides more realistic simulated brightness temperatures and cloud water analysis increments, and improves analysis off the west coasts of the continents by reducing a known bias in stratus. An approximate 10% increase in the use of AMSU-A channels 1–5 and a 12% increase for channel 15 are also observed. The all-sky AMSU-A radiance assimilation became operational in the 4D EnVar GFS system upgrade of 12 May 2016.

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Suranjana Saha, Shrinivas Moorthi, Hua-Lu Pan, Xingren Wu, Jiande Wang, Sudhir Nadiga, Patrick Tripp, Robert Kistler, John Woollen, David Behringer, Haixia Liu, Diane Stokes, Robert Grumbine, George Gayno, Jun Wang, Yu-Tai Hou, Hui-ya Chuang, Hann-Ming H. Juang, Joe Sela, Mark Iredell, Russ Treadon, Daryl Kleist, Paul Van Delst, Dennis Keyser, John Derber, Michael Ek, Jesse Meng, Helin Wei, Rongqian Yang, Stephen Lord, Huug van den Dool, Arun Kumar, Wanqiu Wang, Craig Long, Muthuvel Chelliah, Yan Xue, Boyin Huang, Jae-Kyung Schemm, Wesley Ebisuzaki, Roger Lin, Pingping Xie, Mingyue Chen, Shuntai Zhou, Wayne Higgins, Cheng-Zhi Zou, Quanhua Liu, Yong Chen, Yong Han, Lidia Cucurull, Richard W. Reynolds, Glenn Rutledge, and Mitch Goldberg

The NCEP Climate Forecast System Reanalysis (CFSR) was completed for the 31-yr period from 1979 to 2009, in January 2010. The CFSR was designed and executed as a global, high-resolution coupled atmosphere–ocean–land surface–sea ice system to provide the best estimate of the state of these coupled domains over this period. The current CFSR will be extended as an operational, real-time product into the future. New features of the CFSR include 1) coupling of the atmosphere and ocean during the generation of the 6-h guess field, 2) an interactive sea ice model, and 3) assimilation of satellite radiances by the Gridpoint Statistical Interpolation (GSI) scheme over the entire period. The CFSR global atmosphere resolution is ~38 km (T382) with 64 levels extending from the surface to 0.26 hPa. The global ocean's latitudinal spacing is 0.25° at the equator, extending to a global 0.5° beyond the tropics, with 40 levels to a depth of 4737 m. The global land surface model has four soil levels and the global sea ice model has three layers. The CFSR atmospheric model has observed variations in carbon dioxide (CO2) over the 1979–2009 period, together with changes in aerosols and other trace gases and solar variations. Most available in situ and satellite observations were included in the CFSR. Satellite observations were used in radiance form, rather than retrieved values, and were bias corrected with “spin up” runs at full resolution, taking into account variable CO2 concentrations. This procedure enabled the smooth transitions of the climate record resulting from evolutionary changes in the satellite observing system.

CFSR atmospheric, oceanic, and land surface output products are available at an hourly time resolution and a horizontal resolution of 0.5° latitude × 0.5° longitude. The CFSR data will be distributed by the National Climatic Data Center (NCDC) and NCAR. This reanalysis will serve many purposes, including providing the basis for most of the NCEP Climate Prediction Center's operational climate products by defining the mean states of the atmosphere, ocean, land surface, and sea ice over the next 30-yr climate normal (1981–2010); providing initial conditions for historical forecasts that are required to calibrate operational NCEP climate forecasts (from week 2 to 9 months); and providing estimates and diagnoses of the Earth's climate state over the satellite data period for community climate research.

Preliminary analysis of the CFSR output indicates a product that is far superior in most respects to the reanalysis of the mid-1990s. The previous NCEP–NCAR reanalyses have been among the most used NCEP products in history; there is every reason to believe the CFSR will supersede these older products both in scope and quality, because it is higher in time and space resolution, covers the atmosphere, ocean, sea ice, and land, and was executed in a coupled mode with a more modern data assimilation system and forecast model.

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