Abstract

This paper describes daytime sea surface temperature (SST) climate analyses derived from 16 years (1985–2000) of reprocessed Advanced Very High Resolution Radiometer (AVHRR) Pathfinder Atmospheres (PATMOS) multichannel radiometric data. Two satellite bias correction methods are employed: the first being an aerosol correction, the second being an in situ correction of satellite biases. The aerosol bias correction is derived from observed statistical relationships between the slant-path aerosol optical depth and AVHRR multichannel SST (MCSST) depressions for elevated levels of tropospheric and stratospheric aerosol. Weekly analyses of SST are produced on a 1° equal-angle grid using optimum interpolation (OI) methodology. Four separate OI analyses are derived based on 1) MCSST without satellite bias correction, 2) MCSST with aerosol satellite bias correction, 3) MCSST with in situ correction of satellite biases, and 4) MCSST with both aerosol and in situ corrections of satellite biases. These analyses are compared against the Tropical Rainfall Measuring Mission (TRMM) Microwave Imager OI SST, along with the extended reconstruction SST in situ analysis product. The OI analysis 1 exhibits significant negative and positive biases. Analysis 2, derived exclusively from satellite data, reduces globally the negative bias associated with elevated atmospheric aerosol, and subsequently reveals pronounced variations in diurnal warming consistent with recently published works. Analyses 3 and 4, derived from in situ correction of satellite biases, alleviate biases (positive and negative) associated with both aerosol and diurnal warming, and also reduce the dispersion. The PATMOS OISST 1985–2000 daytime climate analyses presented here provide a high-resolution (1° weekly) empirical database for studying seasonal and interannual climate processes.

Abstract

Eight-year (1990–98), two-satellite (NOAA-11 and -14), global daily ∼(110 km)² gridded observations from the Advanced Very High Resolution Radiometer (AVHRR) Pathfinder Atmosphere (PATMOS) dataset have been previously merged with the Pathfinder Matchup Database (PFMDB) and used to develop the Phase I aerosol correction for sea surface temperatures (SSTs) from AVHRR. In this study, this unique PATMOS–BUOY matchup dataset (N = 105 831) is used to derive and quality control an advanced set of aerosol parameters to be used in the Phase II algorithm: aerosol optical depths in channels 1 (λ ₁ = 0.63 μm) and 2 (λ ₂ = 0.83 μm), τ ₁ and τ ₂, and Ångström exponent α = −ln(τ ₁/τ ₂)/ln(λ ₁/λ ₂). Inaccurate retrievals at low sun and outliers are removed from the data. PATMOS global, multiyear, multisatellite aerosol properties, derived from cloud-free portions of the (110 km)² grid, resemble many features previously observed in the space–time-restricted, (8 km)² resolution Aerosol Observation (AEROBS) operational retrievals, in spite of a different spatial resolution, cloud screening, and sampling. Histograms of τ and α are accurately fit by lognormal and normal probability density functions, respectively. Retrievals of τ ₂ are consistent with τ ₁ at low τ, but reveal high multiplicative bias, resulting in a low additive bias in α. Random errors in α are inversely proportional to τ, with signal-to-noise ratio well approximated as η = τ ₁/τ _1o. Parameter τ _1o (τ threshold at which signal in α compares to its noise, i.e., η = 1) in PATMOS data (τ _1o ∼ 0.11 ± 0.01) is less than in AEROBS (τ _1o ∼ 0.18 ± 0.02), since noise is suppressed by the additional spatial averaging in PATMOS. The effect of cloud screening and sampling is also quantified. PATMOS τ ₁, τ ₂, and α reveal a strong trend against cloud amount, which is not fully understood, and some residual artificial time/angle trends, due to undercorrected calibration errors and remaining algorithm problems. But overall, they show a high degree of self- and interconsistency, thus providing a superior set of aerosol predictors to be used in the Phase II SST aerosol correction algorithm.

Abstract

This paper advances hyperspectral infrared (IR) radiative transfer techniques for retrieving water (ocean and lake) surface skin temperature from clear-sky radiance observations obtained within the longwave atmospheric window region (800–1000 cm⁻¹). High spectral resolution has optimal potential for multispectral algorithms because of the capability to resolve, and thus avoid, gas absorption lines that otherwise obscure the surface signal in conventional narrowband radiometers. A hyperspectral radiative transfer model (RTM) is developed for varying satellite zenith angles, atmospheric profiles (cloud and aerosol free), surface wind speeds and skin temperatures, with atmospheric column transmittance spectra computed from fast models. Wind speed variations in surface emissivity and quasi-specular reflection are both rigorously accounted for. The RTM is then used for deriving retrieval algorithms based upon statistical and physical methodologies. The statistical method is based upon linear regression analyses of brightness temperatures, whereas the physical method is based upon solution of a linear perturbation form of the IR radiative transfer equation valid for window channels. The physical method is unique in its simplicity: It does not solve for atmospheric profiles, but rather relies upon local linearities about guess transmittances for extrapolating the skin temperature. Both algorithms are tested against independent forward calculations and then used to retrieve water surface skin temperatures from the National Polar-orbiting Operational Environmental Satellite System (NPOESS) Airborne Sounder Testbed-Interferometer (NAST-I) flown on board the NASA ER-2. The results demonstrate the capability of hyperspectral radiative transfer for providing an optimal correction for atmospheric gas absorption (viz., water vapor) from the new suite of environmental satellite IR spectrometers.

Abstract

This paper furthers previous investigations into the zenith angular effect of cloud contamination within infrared (IR) window radiance observations commonly used in the retrieval of environmental data records (EDRs). Here analyses were performed of clear-sky forward radiance calculations versus observations obtained under clear to partly cloudy conditions over ocean. The authors utilized high-resolution IR spectra observed by the aircraft-based National Polar-Orbiting Partnership (NPP) Aircraft Sounder Test Bed-Interferometer (NAST-I) during the Joint Airborne Infrared Atmospheric Sounding Interferometer (IASI) Validation Experiment (JAIVEx) and performed forward calculations using collocated dropsondes. An aerosol optical depth EDR product derived from Geostationary Operational Environmental Satellite (GOES) was then applied to detect clouds within NAST-I fields of view (FOVs). To calculate the angular variation of clouds, expressions were derived for estimating cloud aspect ratios from visible imagery where cloud shadow lengths can be estimated relative to cloud horizontal diameters. In agreement with sensitivity calculations, it was found that a small cloud fraction within window radiance observations can have a measurable impact on the angular agreement with clear-sky calculations on the order of 0.1–0.4 K in brightness temperature. It was also found that systematic sun-glint contamination can likewise have an impact on the order of 0.1 K. These results are germane to IR sensor data record (SDR) calibration/validation and EDR retrieval schemes depending upon clear-sky SDRs, as well as radiative transfer modeling involving randomly distributed broken cloud fields.

Abstract

Satellite and in situ measurements of the sea surface and the atmosphere often have inadequate sampling frequencies and often lack consistent global coverage. Because of such limitations, reanalysis model output is frequently used in atmospheric and oceanographic research endeavors to complement satellite and in situ data. The National Aeronautics and Space Administration’s (NASA’s) Goddard Earth Sciences Modern-Era Retrospective Analysis for Research and Applications version 2 (MERRA-2) and the European Centre for Medium-Range Weather Forecasts (ECMWF) interim reanalysis (ERA-Interim) datasets provide accurate, complete fields through the assimilation of many atmospheric and surface observations. Still, the reanalysis output data must be rigorously and continuously evaluated to understand their strengths and weaknesses. To this end, this study evaluates sea surface skin temperature (SST_skin) and atmospheric temperature and humidity profiles in MERRA-2 and ERA-Interim data through comparisons with independent Marine-Atmospheric Emitted Radiance Interferometer (M-AERI) and radiosonde data from the Aerosols and Ocean Science Expeditions (AEROSE) cruises, focusing on the representation of spatial and temporal variability. SST_skin values are generally in good agreement with corresponding M-AERI measurements, with the average differences on the order of 0.1 K. Comparisons between MERRA-2 and ERA-Interim relative humidity and air temperature profiles with a total of 553 radiosondes that have been withheld from data assimilation schemes show good correspondence below 500 hPa: the average air temperature difference is <2 K and the average relative humidity discrepancy is within 10%. These results support the use of these MERRA-2 and ERA-Interim reanalysis fields in a variety of research applications.

Abstract

As part of the joint National Oceanic and Atmospheric Administration–National Aeronautics and Space Administration (NOAA–NASA) Pathfinder program, the NOAA/National Environmental Satellite, Data and Information Service (NESDIS) has created a research-quality atmospheric, climate-scale dataset through the reprocessing of archived Advanced Very High Resolution Radiometer (AVHRR) observations from four afternoon satellites, in orbit since 1981. The raw observations were recalibrated using a vicarious calibration technique for the AVHRR reflectance channels and an improved treatment of the nonlinearity of the three infrared emittance channels. State-of-the-art algorithms are used in the Pathfinder Atmosphere (PATMOS) project to process global AVHRR datasets into statistics of channel radiances, total cloud amount, components of the earth's radiation budget, and aerosol optical thickness over oceans. The radiances and earth radiation budget components are determined for clear-sky and all-sky conditions. The output products are generated on a quasi-equal-area grid with a spatial resolution of approximately 110 km, with twice-a-day temporal resolution, and averaged over 5-day (pentad) and monthly time periods. The quality of the products is assessed relative to independent surface or satellite observations of these parameters. This analysis shows that the PATMOS data are sufficiently accurate for studies of the interaction of clouds and aerosol with solar and terrestrial radiation, and of climatic phenomena with large signals, for example, the annual cycle, monsoons, and the four ENSOs and two major volcanic eruptions that occurred during the 19-yr PATMOS period. Analysis also indicates that smaller climate signals, such as those associated with longer-term trends in surface temperature, may be difficult to detect due to the presence of artifacts in the time series that result from the drift of each satellite's observation time over its mission. However, a simple statistical method is employed to remove much of the effect caused by orbital drift. The uncorrected PATMOS dataset is accessible electronically.

Abstract

Near-real-time satellite-derived temperature and moisture soundings provide information about the changing atmospheric vertical thermodynamic structure occurring between successive routine National Weather Service (NWS) radiosonde launches. In particular, polar-orbiting satellite soundings become critical to the computation of stability indices over the central United States in the midafternoon, when there are no operational NWS radiosonde launches. Accurate measurements of surface temperature and dewpoint temperature are key in the calculation of severe weather indices, including surface-based convective available potential energy (SBCAPE). This paper addresses a shortcoming of current operational infrared-based satellite soundings, which underestimate the surface parcel temperature and dewpoint when CAPE is nonzero. This leads to a systematic underestimate of SBCAPE. This paper demonstrates a merging of satellite-derived vertical profiles with surface observations to address this deficiency for near-real-time applications. The National Oceanic and Atmospheric Administration (NOAA) Center for Environmental Prediction (NCEP) Meteorological Assimilation Data Ingest System (MADIS) hourly surface observation data are blended with satellite soundings derived using the NOAA Unique Combined Atmospheric Processing System (NUCAPS) to create a greatly improved SBCAPE calculation. This study is not intended to validate NUCAPS or the combined NUCAPS + MADIS product, but to demonstrate the benefits of combining observational weather satellite profile data and surface observations. Two case studies, 18 June 2017 and 3 July 2017, are used in this study to illustrate the success of the combined NUCAPS + MADIS SBCAPE compared to the NUCAPS-only SBCAPE estimate. In addition, a 6-month period, April–September 2018, was analyzed to provide a comprehensive analysis of the impact of using surface observations in satellite SBCAPE calculations. To address the need for reduced data latency, a near-real-time merged satellite and surface observation product is demonstrated using NUCAPS products from the Community Satellite Processing Package (CSPP) applied to direct broadcast data received at the University of Wisconsin–Madison, Hampton University in Virginia, and the Naval Research Laboratory in Monterey, California. Through this study, it is found that the combination of the MADIS surface observation data and the NUCAPS satellite profile data improves the SBCAPE estimate relative to comparisons with the Storm Prediction Center (SPC) mesoscale analysis and the NAM analysis compared to the NUCAPS-only SBCAPE estimate. An assessment of the 6-month period between April and September 2018 determined the dry bias in NUCAPS at the surface is the primary cause of the underestimation of the NUCAPS-only SBCAPE estimate.

Abstract

Measurements of the spectra of infrared emission from the atmosphere were taken by a Marine-Atmospheric Emitted Radiance Interferometer (M-AERI) deployed on the NOAA ship Ronald H. Brown during the Aerosol and Ocean Science Expedition (AEROSE) in the tropical Atlantic Ocean from 29 February to 26 March 2004. The spectra are used to retrieve profiles of temperature and humidity in the lower troposphere up to a height of 3000 m. The M-AERI retrievals of the atmospheric structure require an initial guess profile. In this work, retrievals obtained from four separate initializations are compared, using 1) radiosondes launched from the Ronald H. Brown, 2) NOAA/NWS/NCEP model reanalyses, 3) ECMWF model analyses, and 4) ECMWF model forecasts. The performance of the M-AERI retrievals for all four first-guess sources is then evaluated against the radiosonde measurements. The M-AERI retrievals initialized using radiosondes reproduce the radiosonde profiles quite well and capture much of the observed vertical structure as should be expected. Of the retrievals initialized with model fields, those obtained using the ECMWF data yielded results closest to the radiosonde observations and enabled detection of the Saharan air layer (SAL) evident during AEROSE. However, the NCEP reanalysis, as well as the corresponding retrievals, failed to detect the SAL. These results demonstrate the ability of the M-AERI profile retrievals to identify the anomalous humidity distributions in the lower troposphere, but underscore the need for suitable vertical resolution in the first-guess profile used in the retrievals under such conditions.

Abstract

Since 1988, the National Oceanic and Atmospheric Administration (NOAA) has provided operational aerosol observations (AEROBS) from the Advanced Very High Resolution Radiometer (AVHRR/2) on board the afternoon NOAA satellites [nominal equator crossing time, (EXT) ∼1330]. Aerosol optical depth (AOD) has been retrieved over oceans from channel 1 of AVHRR/2 on board NOAA-11 (1988–94) and -14 (1995–2000) using the first- and second-generation algorithms, respectively. With the launch of the NOAA-KLM series of satellites, in particular NOAA-16 (L) in September 2000 (EXT ∼1400), and NOAA-17 (M) in June 2002 (EXT ∼1000), an extended and improved third-generation algorithm was enabled. Like its predecessors, this algorithm continues to employ a single-channel methodology, by which all parameters in the retrieval algorithm (excluding AOD) are set globally as nonvariables. But now, in addition to AOD from channel 1, τ ₁ (λ ₁ = 0.63 μm), the algorithm also retrieves τ ₂ and τ ₃ in AVHRR/3 channels 2 (λ ₂ = 0.83 μm) and 3A (λ ₃ = 1.61 μm). The retrievals are made with more accurate and flexible, satellite- and channel-specific lookup tables generated with the Second Simulation of the Satellite Signal in the Solar Spectrum (6S) radiative transfer code. From pairs of τ _i and τ _j , the Ångstrom exponent (AE) parameters can then be determined as α _ij = −ln(τ _i /τ _j )/ln(λ _i /λ _j ).

This paper describes the AEROBS processing and gives examples of aerosol products, along with a preliminary diagnostics of their quality using some of the previously developed self-consistency checks. Interconsistency between the NOAA-16 and -17 aerosol retrievals is also checked. The AODs are largely coherent but distorted by the AVHRR calibration uncertainties, and subject to noise and outliers. These τ errors, unavoidable in real-time AVHRR processing, severely impact the derived AE, demonstrating a fundamental instability in estimating the aerosol model under typical maritime conditions from AVHRR. Consequently, it is concluded that the robust single-channel retrievals should be continued in the AEROBS operations in the KLM era. The more sophisticated multichannel techniques may be tested while reprocessing historical AVHRR data, only after the data quality issues have been resolved (viz., calibration uncertainties constrained, outliers removed, and noise suppressed by spatial averaging).

As part of the joint National Oceanic and Atmospheric Administration (NOAA) and National Aeronautics and Space Administration (NASA) Pathfinder program, the NOAA National Environmental Satellite, Data, and Information Service (NESDIS) has created a research-quality global atmospheric dataset through the reprocessing of Advanced Very High Resolution Radiometer (AVHRR) observations since 1981. The AVHRR is an imaging radiometer that flies on NOAA polar-orbiting operational environmental satellites (POES) measuring radiation reflected and emitted by the earth in five spectral channels. Raw AVHRR observations were recalibrated using a vicarious calibration technique for the reflectance channels and an appropriate treatment of the nonlinearity of the infrared channels. The observations are analyzed in the Pathfinder Atmosphere (PATMOS) project to obtain statistics of channel radiances, cloud amount, top of the atmosphere radiation budget, and aerosol optical thickness over ocean. The radiances and radiation budget components are determined for clear-sky and all-sky conditions. The output products are generated on a quasi-equalarea grid with an approximate 110 km × 110 km spatial resolution and twice-a-day temporal resolution, and averaged over 5-day (pentad) and monthly time periods. PATMOS data span the period from September 1981 through June 2001. Analyses show that the PATMOS data in their current archived form are sufficiently accurate for studies of the interaction of clouds and aerosol with solar and terrestrial radiation, and of climatic phenomena with large signals (e.g., the annual cycle, monsoons, ENSOs, or major volcanic eruptions). Global maps of the annual average of selected products are displayed to illustrate the capability of the dataset to depict the climatological fields and the spatial detail and relationships between the fields, further demonstrating how PATMOS is a unique resource for climate studies. Smaller climate signals, such as those associated with global warming, may be more difficult to detect due to the presence of artifacts in the time series of the products. Principally, these are caused by the drift of each satellite's observation time over its mission. A statistical method, which removes most of these artifacts, is briefly discussed. Quality of the products is assessed by comparing the adjusted monthly mean time series for each product with those derived from independent satellite observations. The PATMOS dataset for the monthly means is accessible at www.saa.noaa.gov/.