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Abstract
Aerosol optical depths, τ 1 and τ 2, and the Ångström exponent α = –ln(τ 1/τ 2)/ln(λ 1/λ 2), are retrieved from daytime measurements (sun zenith angle θ o < 60°) over ocean in reflectance bands 1 (λ 1 = 0.63 µm) and 2 (λ 2 = 1.61 µm) of the five-channel visible and infrared scanner (VIRS) on board the Tropical Rainfall Measuring Mission (TRMM) satellite. In band 2, a thermal leak originating from the secondary spectral response peak at ∼5.2 µm contributes radiance comparable to the signal scattered by aerosols. In the past two corrections, the thermal signal was parameterized empirically as a linear function of radiances in bands 4 and 5 (centered at 10.8 and 11.9 µm, respectively), R 4 and R 5, and a quadratic function of view angle θ through multiple regression analyses. The regression coefficients were estimated from a limited amount of all-sky nighttime (100° < θ o < 170°) data over land and ocean, and were used to predict and remove the false signal from daytime data. As a result, retrievals of τ 2 and α have been improved, but they still remain seriously flawed.
This study reexamines the nighttime signal in VIRS channel 2 using two representative 9-day segments of the TRMM single scanner footprint (SSF) data collected from 4–12 February and 2–10 April 1998. The past parameterizations did not always perform accurately. Their residuals are biased and skewed, and reveal artificial trends with time, latitude, θ, R 4, and R 5. A new parameterization of the nighttime signal is proposed that makes use of 1) clear-sky ocean data only (rather than previously used all sky, full set); 2) more accurate principal component analyses (PCA) to approximate the θ, R 4, and R 5 dependencies of the false signal (in place of the formerly used liner/quadratic regressions); and 3) explicit accounting for temporal instability of the spurious signal (rather than assuming it to be stable as was done in the past). The new parameterization substantially relieves the problems found in the previous two parameterizations. A much smaller false signal of unknown origin, found in channel 1, is also analyzed and parameterized in this study, consistently with channel 2. The effects of false signals and residuals of different corrections on retrieved τ and α are preliminarily estimated using an approximate formulation based on a simplified treatment of the radiative transfer equation.
Abstract
Aerosol optical depths, τ 1 and τ 2, and the Ångström exponent α = –ln(τ 1/τ 2)/ln(λ 1/λ 2), are retrieved from daytime measurements (sun zenith angle θ o < 60°) over ocean in reflectance bands 1 (λ 1 = 0.63 µm) and 2 (λ 2 = 1.61 µm) of the five-channel visible and infrared scanner (VIRS) on board the Tropical Rainfall Measuring Mission (TRMM) satellite. In band 2, a thermal leak originating from the secondary spectral response peak at ∼5.2 µm contributes radiance comparable to the signal scattered by aerosols. In the past two corrections, the thermal signal was parameterized empirically as a linear function of radiances in bands 4 and 5 (centered at 10.8 and 11.9 µm, respectively), R 4 and R 5, and a quadratic function of view angle θ through multiple regression analyses. The regression coefficients were estimated from a limited amount of all-sky nighttime (100° < θ o < 170°) data over land and ocean, and were used to predict and remove the false signal from daytime data. As a result, retrievals of τ 2 and α have been improved, but they still remain seriously flawed.
This study reexamines the nighttime signal in VIRS channel 2 using two representative 9-day segments of the TRMM single scanner footprint (SSF) data collected from 4–12 February and 2–10 April 1998. The past parameterizations did not always perform accurately. Their residuals are biased and skewed, and reveal artificial trends with time, latitude, θ, R 4, and R 5. A new parameterization of the nighttime signal is proposed that makes use of 1) clear-sky ocean data only (rather than previously used all sky, full set); 2) more accurate principal component analyses (PCA) to approximate the θ, R 4, and R 5 dependencies of the false signal (in place of the formerly used liner/quadratic regressions); and 3) explicit accounting for temporal instability of the spurious signal (rather than assuming it to be stable as was done in the past). The new parameterization substantially relieves the problems found in the previous two parameterizations. A much smaller false signal of unknown origin, found in channel 1, is also analyzed and parameterized in this study, consistently with channel 2. The effects of false signals and residuals of different corrections on retrieved τ and α are preliminarily estimated using an approximate formulation based on a simplified treatment of the radiative transfer equation.
Abstract
The backscatter part of the aerosol phase function P
A
(χ), where χ is the scattering angle, is difficult to measure from the ground. Experimental data for χ > 120° are not reported in the literature. Customarily, P
A
(χ) is calculated from Mie theory using an aerosol size distribution either prescribed or estimated by inversion of spectral or almucantar/aureole measurements. These results clearly require validation using direct measurements. In this paper, an empirical phase function of atmospheric aerosol over the ocean is estimated in backscatter (χ > 130°) from coincident measurements of upward radiance in channel 1 (0.63 μm) of the Advanced Very High Resolution Radiometer (AVHRR) on board National Oceanic and Atmospheric Administration satellites and sun-photometer aerosol optical thickness,
Abstract
The backscatter part of the aerosol phase function P
A
(χ), where χ is the scattering angle, is difficult to measure from the ground. Experimental data for χ > 120° are not reported in the literature. Customarily, P
A
(χ) is calculated from Mie theory using an aerosol size distribution either prescribed or estimated by inversion of spectral or almucantar/aureole measurements. These results clearly require validation using direct measurements. In this paper, an empirical phase function of atmospheric aerosol over the ocean is estimated in backscatter (χ > 130°) from coincident measurements of upward radiance in channel 1 (0.63 μm) of the Advanced Very High Resolution Radiometer (AVHRR) on board National Oceanic and Atmospheric Administration satellites and sun-photometer aerosol optical thickness,
Abstract
This paper outlines the processing stream for aerosol retrievals over oceans from the visible and infrared scanner [VIRS; a five-channel radiometer similar to the National Oceanic and Atmospheric Administration’s Advanced Very High Resolution Radiometer (AVHRR)] aboard the Tropical Rainfall Measuring Mission (TRMM) satellite, launched in November 1997. Emphasis is on 1) the applying the previously developed AVHRR second-generation aerosol retrieval algorithm to VIRS data to derive an aerosol parameter, indicative of particle size; 2) removing the unwanted “thermal leak” signal in the 1.61-μm channel; 3) giving examples of the first aerosol retrievals from space at this wavelength; and 4) assessing the accuracy of the retrievals with theoretical error analyses and empirical self- and interconsistency checks. Aerosol optical depths
Abstract
This paper outlines the processing stream for aerosol retrievals over oceans from the visible and infrared scanner [VIRS; a five-channel radiometer similar to the National Oceanic and Atmospheric Administration’s Advanced Very High Resolution Radiometer (AVHRR)] aboard the Tropical Rainfall Measuring Mission (TRMM) satellite, launched in November 1997. Emphasis is on 1) the applying the previously developed AVHRR second-generation aerosol retrieval algorithm to VIRS data to derive an aerosol parameter, indicative of particle size; 2) removing the unwanted “thermal leak” signal in the 1.61-μm channel; 3) giving examples of the first aerosol retrievals from space at this wavelength; and 4) assessing the accuracy of the retrievals with theoretical error analyses and empirical self- and interconsistency checks. Aerosol optical depths
Abstract
Monitoring of IR Clear-Sky Radiances over Oceans for SST (MICROS) is a Web-based tool to monitor “model minus observation” (M − O) biases in clear-sky brightness temperatures (BTs) and sea surface temperatures (SSTs) produced by the Advanced Clear-Sky Processor for Oceans (ACSPO). Currently, MICROS monitors M − O biases in three Advanced Very High Resolution Radiometer (AVHRR) bands centered at 3.7, 11, and 12 μm for five satellites, NOAA-16, -17, -18, -19 and Meteorological Operational (MetOp)-A. The fast Community Radiative Transfer Model (CRTM) is employed to simulate clear-sky BTs, using Reynolds SST and National Centers for Environmental Prediction Global Forecast System profiles as input. Simulated BTs are used in ACSPO for improving cloud screening, physical SST inversions, and monitoring and validating satellite BTs. The key MICROS objectives are to fully understand and reconcile CRTM and AVHRR BTs, and to minimize cross-platform biases through improvements to ACSPO algorithms, CRTM and its inputs, satellite radiances, and skin-bulk and diurnal SST modeling.
Initially, MICROS was intended for internal use within the National Environmental Satellite, Data, and Information Service (NESDIS) SST team for testing and improving ACSPO products. However, it has quickly outgrown this initial objective and is now used by several research and applications groups. In particular, inclusion of double differences in MICROS has contributed to sensor-to-sensor monitoring within the Global Space-Based Intercalibration System, which is customarily performed using the well-established simultaneous nadir overpass technique. Also, CRTM scientists have made a number of critical improvements to CRTM using MICROS results. They now routinely use MICROS to continuously monitor M − O biases and validate and improve CRTM performance. MICROS is also instrumental in evaluating the accuracy of the first-guess SST and upper-air fields used as input to CRTM. This paper gives examples of these applications and discusses ongoing work and future plans.
Abstract
Monitoring of IR Clear-Sky Radiances over Oceans for SST (MICROS) is a Web-based tool to monitor “model minus observation” (M − O) biases in clear-sky brightness temperatures (BTs) and sea surface temperatures (SSTs) produced by the Advanced Clear-Sky Processor for Oceans (ACSPO). Currently, MICROS monitors M − O biases in three Advanced Very High Resolution Radiometer (AVHRR) bands centered at 3.7, 11, and 12 μm for five satellites, NOAA-16, -17, -18, -19 and Meteorological Operational (MetOp)-A. The fast Community Radiative Transfer Model (CRTM) is employed to simulate clear-sky BTs, using Reynolds SST and National Centers for Environmental Prediction Global Forecast System profiles as input. Simulated BTs are used in ACSPO for improving cloud screening, physical SST inversions, and monitoring and validating satellite BTs. The key MICROS objectives are to fully understand and reconcile CRTM and AVHRR BTs, and to minimize cross-platform biases through improvements to ACSPO algorithms, CRTM and its inputs, satellite radiances, and skin-bulk and diurnal SST modeling.
Initially, MICROS was intended for internal use within the National Environmental Satellite, Data, and Information Service (NESDIS) SST team for testing and improving ACSPO products. However, it has quickly outgrown this initial objective and is now used by several research and applications groups. In particular, inclusion of double differences in MICROS has contributed to sensor-to-sensor monitoring within the Global Space-Based Intercalibration System, which is customarily performed using the well-established simultaneous nadir overpass technique. Also, CRTM scientists have made a number of critical improvements to CRTM using MICROS results. They now routinely use MICROS to continuously monitor M − O biases and validate and improve CRTM performance. MICROS is also instrumental in evaluating the accuracy of the first-guess SST and upper-air fields used as input to CRTM. This paper gives examples of these applications and discusses ongoing work and future plans.
Abstract
The present second-generation aerosol retrieval algorithm over oceans used at NOAA/National Environmental Satellite, Data, and Information Service (NESDIS) separately retrieves two values of aerosol optical depth, τ 1 and τ 2, from Advanced Very High Resolution Radiometer (AVHRR) channels 1 and 2 centered at λ 1 = 0.63 (operational) and λ 2 = 0.83 μm (experimental), respectively. From these, an effective Ångström exponent α, related to particle size, can be derived as α = −ln(τ 1/τ 2)/ln(λ 1/λ 2). The single-channel lookup tables, relating reflectance to optical depth in the retrievals, have been precalculated with the Dave (1973) scalar radiative transfer (RT) model. This first part of a two-part paper describes the retrieval algorithm, with emphasis on its RT modeling related elements, and documents the transition to the Second Simulation of the Satellite Signal in the Solar Spectrum (6S; 1997) RT model. The new 6S RT model has the capability to account for reflection from wind-roughened sea surface, offers a wide choice of flexible aerosol and gaseous absorption models, and allows easy convolution with the sensor's spectral response. The value of these new features for aerosol remote sensing from AVHRR is discussed in detail. The transition effect is quantified by directly applying the Dave- and 6S-based algorithms to four large datasets of NOAA-14 AVHRR measurements, collected between February 1998 and May 1999 over the latitudinal belt of 5°–25°S. Statistics of the differences (δτ = τ Dave − τ 6S and δα = α Dave − α 6S) are as follows: averages − 〈δτ 1〉 < 1 × 10−3, 〈δτ 2〉 ≈ −4 × 10−3, and 〈δα〉 ≈ +8 × 10−2; and standard deviations are στ 1 ∼ 6 × 10−3, στ 2 ∼ 4 × 10−3, and σα ≈ 9 × 10−2. These are found to be well within a few percent of typical values of τ and α and their respective ranges of variability, thus ensuring a smooth transition and continuity in the operational aerosol retrieval. On the other hand, the 6S model provides a much more flexible RT modeling tool compared to the previously used Dave code.
Abstract
The present second-generation aerosol retrieval algorithm over oceans used at NOAA/National Environmental Satellite, Data, and Information Service (NESDIS) separately retrieves two values of aerosol optical depth, τ 1 and τ 2, from Advanced Very High Resolution Radiometer (AVHRR) channels 1 and 2 centered at λ 1 = 0.63 (operational) and λ 2 = 0.83 μm (experimental), respectively. From these, an effective Ångström exponent α, related to particle size, can be derived as α = −ln(τ 1/τ 2)/ln(λ 1/λ 2). The single-channel lookup tables, relating reflectance to optical depth in the retrievals, have been precalculated with the Dave (1973) scalar radiative transfer (RT) model. This first part of a two-part paper describes the retrieval algorithm, with emphasis on its RT modeling related elements, and documents the transition to the Second Simulation of the Satellite Signal in the Solar Spectrum (6S; 1997) RT model. The new 6S RT model has the capability to account for reflection from wind-roughened sea surface, offers a wide choice of flexible aerosol and gaseous absorption models, and allows easy convolution with the sensor's spectral response. The value of these new features for aerosol remote sensing from AVHRR is discussed in detail. The transition effect is quantified by directly applying the Dave- and 6S-based algorithms to four large datasets of NOAA-14 AVHRR measurements, collected between February 1998 and May 1999 over the latitudinal belt of 5°–25°S. Statistics of the differences (δτ = τ Dave − τ 6S and δα = α Dave − α 6S) are as follows: averages − 〈δτ 1〉 < 1 × 10−3, 〈δτ 2〉 ≈ −4 × 10−3, and 〈δα〉 ≈ +8 × 10−2; and standard deviations are στ 1 ∼ 6 × 10−3, στ 2 ∼ 4 × 10−3, and σα ≈ 9 × 10−2. These are found to be well within a few percent of typical values of τ and α and their respective ranges of variability, thus ensuring a smooth transition and continuity in the operational aerosol retrieval. On the other hand, the 6S model provides a much more flexible RT modeling tool compared to the previously used Dave code.
Abstract
This second part of a two-part study evaluates retrievals of aerosol optical depths, τ 1 and τ 2, in Advanced Very High Resolution Radiometer (AVHRR) channels 1 and 2 centered at λ 1 = 0.63 and λ 2 = 0.83 μm, and an effective Ångström exponent, α, derived therefrom as α = −ln(τ 1/τ 2)/ln(λ 1/λ 2). The retrievals are made with the Second Simulation of the Satellite Signal in the Solar Spectrum (6S) radiative transfer model from four NOAA-14 AVHRR datasets, collected between February 1998 and May 1999 in the latitudinal belt of 5°–25°S. A series of quality control (QC) checks applied to the retrievals to identify outliers are described. These remove a total of ∼1% of points, which presumably originate from channel misregistration, residual cloud in AVHRR cloud-screened pixels, and substantial deviations from the assumptions used in the retrieval model (e.g., bright coastal and high altitude inland waters). First, from examining histograms of the derived parameters it is found that τ and α are accurately fit by lognormal and normal probability distribution functions (PDFs), respectively. Second, the scattergrams τ 1 versus τ 2 are analyzed to see if they form a coherent pattern. They do indeed converge at the origin, as expected, but frequently are outside of the expected domain in τ 1–τ 2 space, defined by two straight lines corresponding to α = 0 and α = 2. This results in a low bias in α, which tends to fill in an interval of α ∈ [−1, 1] rather than α ∈ [0, 2]. Third, scattergrams of α versus τ are used to empirically confirm a previously drawn theoretical conclusion that errors in α are inversely proportional to τ. More in-depth quantitative analyses suggest that the AVHRR-derived Ångström exponent becomes progressively more meaningful when τ > 0.2. Geographical trends are studied to demonstrate that the selected ocean area is reasonably uniform to justify application of consistency checks to reveal angular trends in the retrievals. These checks show that in most cases, the artifacts in the retrieved τ and α are statistically insignificant. On average, the analyses suggest that the retrieved τ 1, τ 2, and α show a high degree of self- and interconsistency, with the exception of a troublesome May 1999 dataset. The most prominent problem noticed so far is the inconsistency between τ 1 and τ 2, persistent from one dataset to another, which calls for fine-tuning some (not aerosol-model related) elements of the retrieval algorithm. These adjustments will be discussed elsewhere.
Abstract
This second part of a two-part study evaluates retrievals of aerosol optical depths, τ 1 and τ 2, in Advanced Very High Resolution Radiometer (AVHRR) channels 1 and 2 centered at λ 1 = 0.63 and λ 2 = 0.83 μm, and an effective Ångström exponent, α, derived therefrom as α = −ln(τ 1/τ 2)/ln(λ 1/λ 2). The retrievals are made with the Second Simulation of the Satellite Signal in the Solar Spectrum (6S) radiative transfer model from four NOAA-14 AVHRR datasets, collected between February 1998 and May 1999 in the latitudinal belt of 5°–25°S. A series of quality control (QC) checks applied to the retrievals to identify outliers are described. These remove a total of ∼1% of points, which presumably originate from channel misregistration, residual cloud in AVHRR cloud-screened pixels, and substantial deviations from the assumptions used in the retrieval model (e.g., bright coastal and high altitude inland waters). First, from examining histograms of the derived parameters it is found that τ and α are accurately fit by lognormal and normal probability distribution functions (PDFs), respectively. Second, the scattergrams τ 1 versus τ 2 are analyzed to see if they form a coherent pattern. They do indeed converge at the origin, as expected, but frequently are outside of the expected domain in τ 1–τ 2 space, defined by two straight lines corresponding to α = 0 and α = 2. This results in a low bias in α, which tends to fill in an interval of α ∈ [−1, 1] rather than α ∈ [0, 2]. Third, scattergrams of α versus τ are used to empirically confirm a previously drawn theoretical conclusion that errors in α are inversely proportional to τ. More in-depth quantitative analyses suggest that the AVHRR-derived Ångström exponent becomes progressively more meaningful when τ > 0.2. Geographical trends are studied to demonstrate that the selected ocean area is reasonably uniform to justify application of consistency checks to reveal angular trends in the retrievals. These checks show that in most cases, the artifacts in the retrieved τ and α are statistically insignificant. On average, the analyses suggest that the retrieved τ 1, τ 2, and α show a high degree of self- and interconsistency, with the exception of a troublesome May 1999 dataset. The most prominent problem noticed so far is the inconsistency between τ 1 and τ 2, persistent from one dataset to another, which calls for fine-tuning some (not aerosol-model related) elements of the retrieval algorithm. These adjustments will be discussed elsewhere.
Abstract
The Suomi National Polar-Orbiting Partnership (S-NPP) satellite was successfully launched on 28 October 2011. It carries five new-generation instruments, including the Visible Infrared Imaging Radiometer Suite (VIIRS). The VIIRS is a whiskbroom radiometer that scans the surface of the earth using a rotating telescope assembly, a double-sided half-angle mirror, and 16 individual detectors. Substantial efforts are being made to accurately calibrate all detectors in orbit. As of this writing, VIIRS striping is reduced to levels below those seen in corresponding Terra and Aqua Moderate Resolution Imaging Spectroradiometer (MODIS) bands and meets the program specifications and requirements. However, the level 2 SST products derived from level 1 sensor data records (SDRs) thermal emissive bands still show residual striping. These artifacts reduce the accuracy of SST measurements and adversely affect cloud masking and the output of downstream applications, such as thermal front detection. To improve the quality of SST imagery derived from the VIIRS sensor, an adaptive algorithm was developed for operational use within the National Environmental Satellite, Data, and Information Service (NESDIS)’s SST system. The methodology uses a unidirectional quadratic variational model to extract stripe noise from the observed image prior to nonlocal filtering. Evaluation of the algorithm performance over an extended dataset demonstrates a significant improvement in the Advanced Clear-Sky Processor for Oceans (ACSPO) VIIRS SST image quality, with normalized improvement factors (NIF) varying between 5% and 25%.
Abstract
The Suomi National Polar-Orbiting Partnership (S-NPP) satellite was successfully launched on 28 October 2011. It carries five new-generation instruments, including the Visible Infrared Imaging Radiometer Suite (VIIRS). The VIIRS is a whiskbroom radiometer that scans the surface of the earth using a rotating telescope assembly, a double-sided half-angle mirror, and 16 individual detectors. Substantial efforts are being made to accurately calibrate all detectors in orbit. As of this writing, VIIRS striping is reduced to levels below those seen in corresponding Terra and Aqua Moderate Resolution Imaging Spectroradiometer (MODIS) bands and meets the program specifications and requirements. However, the level 2 SST products derived from level 1 sensor data records (SDRs) thermal emissive bands still show residual striping. These artifacts reduce the accuracy of SST measurements and adversely affect cloud masking and the output of downstream applications, such as thermal front detection. To improve the quality of SST imagery derived from the VIIRS sensor, an adaptive algorithm was developed for operational use within the National Environmental Satellite, Data, and Information Service (NESDIS)’s SST system. The methodology uses a unidirectional quadratic variational model to extract stripe noise from the observed image prior to nonlocal filtering. Evaluation of the algorithm performance over an extended dataset demonstrates a significant improvement in the Advanced Clear-Sky Processor for Oceans (ACSPO) VIIRS SST image quality, with normalized improvement factors (NIF) varying between 5% and 25%.
Abstract
The quality of in situ sea surface temperatures (SSTs) is critical for calibration and validation of satellite SSTs. In situ SSTs come from different countries, agencies, and platforms. As a result, their quality is often suboptimal, nonuniform, and measurement-type specific. This paper describes a system developed at the National Oceanic and Atmospheric Administration (NOAA), the in situ SST Quality Monitor (iQuam; www.star.nesdis.noaa.gov/sod/sst/iquam/). It performs three major functions with the Global Telecommunication System (GTS) data: 1) quality controls (QC) in situ SSTs, using Bayesian reference and buddy checks similar to those adopted in the Met Office, in addition to providing basic screenings, such as duplicate removal, plausibility, platform track, and SST spike checks; 2) monitors quality-controlled SSTs online, in near–real time; and 3) serves reformatted GTS SST data to NOAA and external users with quality flags appended. Currently, iQuam’s web page displays global monthly maps of measurement locations stratified by four in situ platform types (drifters, ships, and tropical and coastal moorings) as well as their corresponding “in situ minus reference” SST statistics. Time series of all corresponding SST and QC statistics are also trended. The web page user can also monitor individual in situ platforms. The current status of iQuam and ongoing improvements are discussed.
Abstract
The quality of in situ sea surface temperatures (SSTs) is critical for calibration and validation of satellite SSTs. In situ SSTs come from different countries, agencies, and platforms. As a result, their quality is often suboptimal, nonuniform, and measurement-type specific. This paper describes a system developed at the National Oceanic and Atmospheric Administration (NOAA), the in situ SST Quality Monitor (iQuam; www.star.nesdis.noaa.gov/sod/sst/iquam/). It performs three major functions with the Global Telecommunication System (GTS) data: 1) quality controls (QC) in situ SSTs, using Bayesian reference and buddy checks similar to those adopted in the Met Office, in addition to providing basic screenings, such as duplicate removal, plausibility, platform track, and SST spike checks; 2) monitors quality-controlled SSTs online, in near–real time; and 3) serves reformatted GTS SST data to NOAA and external users with quality flags appended. Currently, iQuam’s web page displays global monthly maps of measurement locations stratified by four in situ platform types (drifters, ships, and tropical and coastal moorings) as well as their corresponding “in situ minus reference” SST statistics. Time series of all corresponding SST and QC statistics are also trended. The web page user can also monitor individual in situ platforms. The current status of iQuam and ongoing improvements are discussed.
Abstract
Monthly mean diurnal cycles (MDCs) of surface temperatures over land, represented in 3-h universal time intervals, have been analyzed. Satellite near-global data from the International Satellite Cloud Climatology Project (ISCCP) with a (280 km)2 resolution (C-2 product) are available for seven individual years and as a climatology derived thereof. Surface 19-yr climatologies on ground and air temperatures, separately for all-sky and clear-sky conditions, matched with the ISCCP data, are employed to better understand satellite-derived MDCs.
The MDCs have been converted to local solar time, refined to a regular 1-h time grid using cubic splines, and subjected to principal component analysis. The first two modes approximate MDCs in air and ground–satellite temperatures with rmse’s of about σ = 0.5° and 1°C, respectively, and these accuracies are improved by 20%–35% if the third mode is added. This suggests that two to three temperature measurements during the day allow reconstruction of the full MDC. In the case of two modes, optimal observation times are close to the occurrence of minimum and maximum temperatures, T min and T max. The authors provide an empirical algorithm for reconstructing the full MDC using T min and T max, and estimate its accuracy. In the analyzed match-up dataset, the statistical structure of ground temperature for all-sky conditions most closely resembles that of the ISCCP derived temperature. The results are potentially useful for climate- and global-scale studies and applications.
Abstract
Monthly mean diurnal cycles (MDCs) of surface temperatures over land, represented in 3-h universal time intervals, have been analyzed. Satellite near-global data from the International Satellite Cloud Climatology Project (ISCCP) with a (280 km)2 resolution (C-2 product) are available for seven individual years and as a climatology derived thereof. Surface 19-yr climatologies on ground and air temperatures, separately for all-sky and clear-sky conditions, matched with the ISCCP data, are employed to better understand satellite-derived MDCs.
The MDCs have been converted to local solar time, refined to a regular 1-h time grid using cubic splines, and subjected to principal component analysis. The first two modes approximate MDCs in air and ground–satellite temperatures with rmse’s of about σ = 0.5° and 1°C, respectively, and these accuracies are improved by 20%–35% if the third mode is added. This suggests that two to three temperature measurements during the day allow reconstruction of the full MDC. In the case of two modes, optimal observation times are close to the occurrence of minimum and maximum temperatures, T min and T max. The authors provide an empirical algorithm for reconstructing the full MDC using T min and T max, and estimate its accuracy. In the analyzed match-up dataset, the statistical structure of ground temperature for all-sky conditions most closely resembles that of the ISCCP derived temperature. The results are potentially useful for climate- and global-scale studies and applications.
Evaluation of the In Situ Sea Surface Temperature Quality Control in the NOAA In Situ SST Quality Monitor (iQuam) SystemAbstract
In situ sea surface temperature (SST) measurements play a critical role in the calibration/validation (Cal/Val) of satellite SST retrievals and ocean data assimilation. However, their quality is not always optimal, and proper quality control (QC) is required before they can be used with confidence. The in situ SST Quality Monitor (iQuam) system was established at the National Oceanic and Atmospheric Administration (NOAA) in 2009, initially to support the Cal/Val of NOAA satellite SST products. It collects in situ SST data from multiple sources, performs uniform QC, monitors the QCed data online, and distributes them to users. In this study, the iQuam QC is compared with other QC methods available in some of the in situ data ingested in iQuam. Overall, the iQuam QC performs well on daily to monthly time scales over most global oceans and under a wide variety of environmental conditions. However, it may be less accurate in the daytime, when a pronounced diurnal cycle is present, and in dynamic regions, because of the strong reliance on the “reference SST check,” which employs daily low-resolution level-4 analyses with no diurnal cycle resolved. The iQuam “performance history check,” applied to all in situ platforms, is an effective alternative to the customary “black/gray” lists, available only for some platforms (e.g., drifters and Argo floats). In the future, iQuam QC will be upgraded [e.g., using improved reference field(s), with enhanced temporal and spatial resolutions]. More comparisons with external QC methods will be performed to learn and employ the best QC practices.
Abstract
In situ sea surface temperature (SST) measurements play a critical role in the calibration/validation (Cal/Val) of satellite SST retrievals and ocean data assimilation. However, their quality is not always optimal, and proper quality control (QC) is required before they can be used with confidence. The in situ SST Quality Monitor (iQuam) system was established at the National Oceanic and Atmospheric Administration (NOAA) in 2009, initially to support the Cal/Val of NOAA satellite SST products. It collects in situ SST data from multiple sources, performs uniform QC, monitors the QCed data online, and distributes them to users. In this study, the iQuam QC is compared with other QC methods available in some of the in situ data ingested in iQuam. Overall, the iQuam QC performs well on daily to monthly time scales over most global oceans and under a wide variety of environmental conditions. However, it may be less accurate in the daytime, when a pronounced diurnal cycle is present, and in dynamic regions, because of the strong reliance on the “reference SST check,” which employs daily low-resolution level-4 analyses with no diurnal cycle resolved. The iQuam “performance history check,” applied to all in situ platforms, is an effective alternative to the customary “black/gray” lists, available only for some platforms (e.g., drifters and Argo floats). In the future, iQuam QC will be upgraded [e.g., using improved reference field(s), with enhanced temporal and spatial resolutions]. More comparisons with external QC methods will be performed to learn and employ the best QC practices.
