Global clear-sky surface skin temperature from multiple satellites using a single-channel algorithm with viewing zenith angle correction

Abstract. Surface skin temperature (Ts) is an important parameter for characterizing the energy exchange at the ground/water-atmosphere interface. The Satellite ClOud and Radiation Property retrieval System (SatCORPS) employs a single-channel thermal-infrared- (TIR-) method to retrieve Ts over clear-sky land and ocean surfaces from data taken by geostationary-Earth orbit (GEO) satellite and low-Earth orbit (LEO) satellite imagers. GEO satellites can provide somewhat continuous estimates of Ts over the diurnal cycle in non-polar regions, while polar Ts retrievals from LEO imagers, such as the Advanced Very High Resolution Radiometer (AVHRR) can complement the GEO measurements. The combined global coverage of remotely sensed Ts, along with accompanying cloud and surface radiation parameters, produced in near-real time and from historical satellite data, should be beneficial for both weather and climate applications. For example, near-real-time hourly Ts observations can be assimilated in high-temporal resolution numerical weather prediction models and historical observations can be used for validation or assimilation of climate models. Key drawbacks to the utility of TIR-derived Ts, data include the limitation to clear-sky conditions, the reliance on a particular set of analyses/reanalyses necessary for atmospheric corrections, and the dependence on viewing angle. Therefore, Ts validation with established references is essential, as is proper evaluation of Ts sensitivity to atmospheric correction source. This article presents improvements on the NASA Langley GEO satellite and AVHRR TIR-based Ts product, derived using a single-channel technique. The resulting clear-sky skin temperature values are validated with surface references and independent satellite products. Furthermore, an empirical means of correcting for the viewing-angle dependency of satellite land surface temperature (LST) is explained and validated. Application of a daytime nadir-normalization model yields improved accuracy and precision of GOES-13 LST relative to independent Moderate-resolution Imaging Spectroradiometer (MYD11_L2) LST and Atmospheric Radiation Measurement Program/NOAA ESRL Surface Radiation network ground stations. These corrections serve as a basis for a means to improve satellite-based LST accuracy, thereby leading to better monitoring and utilization of the data. The immediate availability and broad coverage of these skin temperature observations should prove valuable to modelers and climate researchers looking for improved forecasts and better understanding of the global climate model.

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Global clear-sky surface skin temperature from multiple satellites using a single-channel algorithm with angular anisotropy corrections

Atmospheric Measurement Techniques ◽

10.5194/amt-10-351-2017 ◽

2017 ◽

Vol 10 (1) ◽

pp. 351-371 ◽

Cited By ~ 4

Author(s):

Benjamin R. Scarino ◽

Patrick Minnis ◽

Thad Chee ◽

Kristopher M. Bedka ◽

Christopher R. Yost ◽

...

Keyword(s):

Skin Temperature ◽

Single Channel ◽

Global Climate Model ◽

Weather Prediction ◽

Surface Radiation ◽

High Temporal Resolution ◽

Earth Orbit ◽

Angular Anisotropy ◽

Clear Sky ◽

Surface Skin Temperature

Abstract. Surface skin temperature (Ts) is an important parameter for characterizing the energy exchange at the ground/water–atmosphere interface. The Satellite ClOud and Radiation Property retrieval System (SatCORPS) employs a single-channel thermal-infrared (TIR) method to retrieve Ts over clear-sky land and ocean surfaces from data taken by geostationary Earth orbit (GEO) and low Earth orbit (LEO) satellite imagers. GEO satellites can provide somewhat continuous estimates of Ts over the diurnal cycle in non-polar regions, while polar Ts retrievals from LEO imagers, such as the Advanced Very High Resolution Radiometer (AVHRR), can complement the GEO measurements. The combined global coverage of remotely sensed Ts, along with accompanying cloud and surface radiation parameters, produced in near-realtime and from historical satellite data, should be beneficial for both weather and climate applications. For example, near-realtime hourly Ts observations can be assimilated in high-temporal-resolution numerical weather prediction models and historical observations can be used for validation or assimilation of climate models. Key drawbacks to the utility of TIR-derived Ts data include the limitation to clear-sky conditions, the reliance on a particular set of analyses/reanalyses necessary for atmospheric corrections, and the dependence on viewing and illumination angles. Therefore, Ts validation with established references is essential, as is proper evaluation of Ts sensitivity to atmospheric correction source.This article presents improvements on the NASA Langley GEO satellite and AVHRR TIR-based Ts product that is derived using a single-channel technique. The resulting clear-sky skin temperature values are validated with surface references and independent satellite products. Furthermore, an empirically adjusted theoretical model of satellite land surface temperature (LST) angular anisotropy is tested to improve satellite LST retrievals. Application of the anisotropic correction yields reduced mean bias and improved precision of GOES-13 LST relative to independent Moderate-resolution Imaging Spectroradiometer (MYD11_L2) LST and Atmospheric Radiation Measurement Program ground station measurements. It also significantly reduces inter-satellite differences between LSTs retrieved simultaneously from two different imagers. The implementation of these universal corrections into the SatCORPS product can yield significant improvement in near-global-scale, near-realtime, satellite-based LST measurements. The immediate availability and broad coverage of these skin temperature observations should prove valuable to modelers and climate researchers looking for improved forecasts and better understanding of the global climate model.

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Retrieving Clear-Sky Surface Skin Temperature for Numerical Weather Prediction Applications from Geostationary Satellite Data

Remote Sensing ◽

10.3390/rs5010342 ◽

2013 ◽

Vol 5 (1) ◽

pp. 342-366 ◽

Cited By ~ 13

Author(s):

Benjamin Scarino ◽

Patrick Minnis ◽

Rabindra Palikonda ◽

Rolf Reichle ◽

Daniel Morstad ◽

...

Keyword(s):

Skin Temperature ◽

Numerical Weather Prediction ◽

Satellite Data ◽

Weather Prediction ◽

Geostationary Satellite ◽

Numerical Weather ◽

Clear Sky ◽

Surface Skin Temperature

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McClear: a new model estimating downwelling solar radiation at ground level in clear-sky conditions

Atmospheric Measurement Techniques ◽

10.5194/amt-6-2403-2013 ◽

2013 ◽

Vol 6 (9) ◽

pp. 2403-2418 ◽

Cited By ~ 154

Author(s):

M. Lefèvre ◽

A. Oumbe ◽

P. Blanc ◽

B. Espinar ◽

B. Gschwind ◽

...

Keyword(s):

Correlation Coefficient ◽

Weather Prediction ◽

Ground Level ◽

Surface Radiation ◽

Radiative Transfer Model ◽

Transfer Model ◽

Clear Sky ◽

Sky Conditions ◽

Aerosol Properties

Abstract. A new fast clear-sky model called McClear was developed to estimate the downwelling shortwave direct and global irradiances received at ground level under clear skies. It is a fully physical model replacing empirical relations or simpler models used before. It exploits the recent results on aerosol properties, and total column content in water vapour and ozone produced by the MACC project (Monitoring Atmosphere Composition and Climate). It accurately reproduces the irradiance computed by the libRadtran reference radiative transfer model with a computational speed approximately 105 times greater by adopting the abaci, or look-up table, approach combined with interpolation functions. It is therefore suited for geostationary satellite retrievals or numerical weather prediction schemes with many pixels or grid points, respectively. McClear irradiances were compared to 1 min measurements made in clear-sky conditions at several stations within the Baseline Surface Radiation Network in various climates. The bias for global irradiance comprises between −6 and 25 W m−2. The RMSE ranges from 20 W m−2 (3% of the mean observed irradiance) to 36 W m−2 (5%) and the correlation coefficient ranges between 0.95 and 0.99. The bias for the direct irradiance comprises between −48 and +33 W m−2. The root mean square error (RMSE) ranges from 33 W m−2 (5%) to 64 W m−2 (10%). The correlation coefficient ranges between 0.84 and 0.98. This work demonstrates the quality of the McClear model combined with MACC products, and indirectly the quality of the aerosol properties modelled by the MACC reanalysis.

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Validation of Avhrr and Modis ice Surface temperature products using in Situ radiometers

Annals of Glaciology ◽

10.3189/172756406781811457 ◽

2006 ◽

Vol 44 ◽

pp. 345-351 ◽

Cited By ~ 25

Author(s):

Ted A. Scambos ◽

Terry M. Haran ◽

Robert Massom

Keyword(s):

Sea Ice ◽

Surface Temperature ◽

Skin Temperature ◽

Mixed Pixel ◽

Clear Sky ◽

Helicopter Flight ◽

Ice Surface ◽

Surface Skin Temperature ◽

Ice Surface Temperature

AbstractShip-borne and airborne infrared radiometric measurements during the Arise cruise of September–October 2003 permitted in Situ validation Studies of two Satellite-based ice Surface Skin temperature algorithms: the AVHRR Polar Pathfinder Ice Surface Temperature and the MODIS Sea Ice Surface Temperature. Observations of Sea ice from the Aurora Australis Ship’s rail using a KT-19.82 radiometer were conducted between 25 September and 21 October during clear-sky overflights by AVHRR (41 passes) and MODIS (17 passes) on their respective Satellite platforms. Data from both Sensors Show highly linear fits to 1 min integrated radiometer Spot measurements, Spanning the range 245–270 K with a ±1.4˚C, 1σ (AVHRR) and ±1.0˚C (MODIS) variation relative to a 1: 1 relationship. There was no Significant offset. Helicopter observations made with a KT-19.85 radiometer on three dates (8, 19 and 20 October) provided more data (236 gridcell Sites total), but over a more limited Sea-ice Skin temperature range (252–268 K), with higher variation (±1.7˚C, 1σ) due to mixed-pixel issues. Comparison of MODIS and AVHRR algorithms directly, with both images acquired during a helicopter flight, indicates very high correlation and near-unity Slope for the two Satellite-based algorithms. Ship air-temperature data during the validation indicated moderate to Strong inversions over Sea ice under clear Skies. These formed and decayed rapidly (tens of minutes) as clouds moved out of and into the zenith area.

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The HIRLAM fast radiation scheme for mesoscale numerical weather prediction models

Advances in Science and Research ◽

10.5194/asr-14-195-2017 ◽

2017 ◽

Vol 14 ◽

pp. 195-215 ◽

Cited By ~ 3

Author(s):

Laura Rontu ◽

Emily Gleeson ◽

Petri Räisänen ◽

Kristian Pagh Nielsen ◽

Hannu Savijärvi ◽

...

Keyword(s):

Numerical Weather Prediction ◽

Prediction Models ◽

Weather Prediction ◽

Surface Fluxes ◽

Surface Radiation ◽

Numerical Weather ◽

Clear Sky ◽

Radiation Output ◽

Dry Conditions ◽

Nwp Model

Abstract. This paper provides an overview of the HLRADIA shortwave (SW) and longwave (LW) broadband radiation schemes used in the HIRLAM numerical weather prediction (NWP) model and available in the HARMONIE-AROME mesoscale NWP model. The advantage of broadband, over spectral, schemes is that they can be called more frequently within the model, without compromising on computational efficiency. In mesoscale models fast interactions between clouds and radiation and the surface and radiation can be of greater importance than accounting for the spectral details of clear-sky radiation; thus calling the routines more frequently can be of greater benefit than the deterioration due to loss of spectral details. Fast but physically based radiation parametrizations are expected to be valuable for high-resolution ensemble forecasting, because as well as the speed of their execution, they may provide realistic physical perturbations. Results from single-column diagnostic experiments based on CIRC benchmark cases and an evaluation of 10 years of radiation output from the FMI operational archive of HIRLAM forecasts indicate that HLRADIA performs sufficiently well with respect to the clear-sky downwelling SW and longwave LW fluxes at the surface. In general, HLRADIA tends to overestimate surface fluxes, with the exception of LW fluxes under cold and dry conditions. The most obvious overestimation of the surface SW flux was seen in the cloudy cases in the 10-year comparison; this bias may be related to using a cloud inhomogeneity correction, which was too large. According to the CIRC comparisons, the outgoing LW and SW fluxes at the top of atmosphere are mostly overestimated by HLRADIA and the net LW flux is underestimated above clouds. The absorption of SW radiation by the atmosphere seems to be underestimated and LW absorption seems to be overestimated. Despite these issues, the overall results are satisfying and work on the improvement of HLRADIA for the use in HARMONIE-AROME NWP system is ongoing. In a HARMONIE-AROME 3-D forecast experiment we have shown that the frequency of the call for the radiation parametrization and choice of the parametrization scheme makes a difference to the surface radiation fluxes and changes the spatial distribution of the vertically integrated cloud cover and precipitation.

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Satellite Retrieval of Microwave Land Surface Emissivity under Clear and Cloudy Skies in China Using Observations from AMSR-E and MODIS

Remote Sensing ◽

10.3390/rs13193980 ◽

2021 ◽

Vol 13 (19) ◽

pp. 3980

Author(s):

Jiheng Hu ◽

Yuyun Fu ◽

Peng Zhang ◽

Qilong Min ◽

Zongting Gao ◽

...

Keyword(s):

Skin Temperature ◽

Optical Depth ◽

Air Temperature ◽

Land Surface ◽

Surface Emissivity ◽

Primary Input ◽

Near Surface ◽

Clear Sky ◽

Land Surface Emissivity ◽

Surface Skin Temperature

Microwave land surface emissivity (MLSE) is an important geophysical parameter to determine the microwave radiative transfer over land and has broad applications in satellite remote sensing of atmospheric parameters (e.g., precipitation, cloud properties), land surface parameters (e.g., soil moisture, vegetation properties), and the parameters of interactions between atmosphere and terrestrial ecosystem (e.g., evapotranspiration rate, gross primary production rate). In this study, MLSE in China under both clear and cloudy sky conditions was retrieved using satellite passive microwave measurements from Aqua Advanced Microwave Scanning Radiometer-Earth Observing System (AMSR-E), combined with visible/infrared observations from Aqua Moderate Resolution Imaging Spectroradiometer (MODIS), and the European Centre for Medium-Range Weather Forecasts (ECMWF) atmosphere reanalysis dataset of ERA-20C. Attenuations from atmospheric oxygen and water vapor, as well as the emissions and scatterings from cloud particles are taken into account using a microwave radiation transfer model to do atmosphere corrections. All cloud parameters needed are derived from MODIS visible and infrared instantaneous measurements. Ancillary surface skin temperature as well as atmospheric temperature-humidity profiles are collected from ECMWF reanalysis data. Quality control and sensitivity analyses were conducted for the input variables of surface skin temperature, air temperature, and atmospheric humidity. The ground-based validations show acceptable biases of primary input parameters (skin temperature, 2 m air temperature, near surface relative humidity, rain flag) for retrieving using. The subsequent sensitivity tests suggest that 10 K bias of skin temperature or observed brightness temperature may result in a 4% (~0.04) or 7% (0.07) retrieving error in MLSE at 23.5 GHz. A nonlinear sensitivity in the same magnitude is found for air temperature perturbation, while the sensitivity is less than 1% for 300 g/m2 error in cloud water path. Results show that our algorithm can successfully retrieve MLSE over 90% of the satellite detected land surface area in a typical cloudy day (cloud fraction of 64%), which is considerably higher than that of the 29% area by the clear-sky only algorithms. The spatial distribution of MLSE in China is highly dependent on the land surface types and topography. The retrieved MLSE is assessed by compared with other existing clear-sky AMSR-E emissivity products and the vegetation optical depth (VOD) product. Overall, high consistencies are shown for the MLSE retrieved in this study with other AMSR-E emissivity products across China though noticeable discrepancies are observed in Tibetan Plateau and Qinling-Taihang Mountains due to different sources of input skin temperature. In addition, the retrieved MLSE exhibits strong positive correlations in spatial patterns with microwave vegetation optical depth reported in the literature.

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McClear: a new model estimating downwelling solar radiation at ground level in clear-sky conditions

Atmospheric Measurement Techniques Discussions ◽

10.5194/amtd-6-3367-2013 ◽

2013 ◽

Vol 6 (2) ◽

pp. 3367-3405 ◽

Cited By ~ 3

Author(s):

M. Lefèvre ◽

A. Oumbe ◽

P. Blanc ◽

B. Espinar ◽

B. Gschwind ◽

...

Keyword(s):

Weather Prediction ◽

Physical Modelling ◽

Ground Level ◽

Surface Radiation ◽

Radiative Transfer Model ◽

Transfer Model ◽

Clear Sky ◽

Sky Conditions ◽

Aerosol Properties

Abstract. A new fast clear-sky model called McClear was developed to estimate the downwelling shortwave direct and global irradiances received at ground level under clear skies. McClear implements a fully physical modelling replacing empirical relations or simpler models used before. It exploits the recent results on aerosol properties, and total column content in water vapor and ozone produced by the MACC project (Monitoring Atmosphere Composition and Climate). It accurately reproduces the irradiance computed by the libRadtran reference radiative transfer model with a computational speed approximately 105 times greater by adopting the abaci, or look-up tables, approach combined with interpolation functions. It is therefore suited for geostationary satellite retrievals or numerical weather prediction schemes with many pixels or grid points, respectively. McClear irradiances were compared to 1 min measurements made in clear-sky conditions in several stations within the Baseline Surface Radiation Network in various climates. For global, respectively direct, irradiance, the correlation coefficient ranges between 0.95 and 0.99, resp. 0.86 and 0.99. The bias is comprised between −14 and 25 W m−2, resp. −49 and +33 W m−2. The RMSE ranges between 20 W m−2 (3% of the mean observed irradiance) and 36 W m−2 (5%), resp. 33 W m−2 (5%) and 64 W m−2 (10%). These results are much better than those from state-of-the-art models. This work demonstrates the quality of the McClear model combined with MACC products, and indirectly the quality of the aerosol properties modeled by the MACC reanalysis.

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Using SURFRAD to Verify the NOAA Single-Channel Land Surface Temperature Algorithm

Journal of Atmospheric and Oceanic Technology ◽

10.1175/jtech-d-13-00051.1 ◽

2013 ◽

Vol 30 (12) ◽

pp. 2868-2884 ◽

Cited By ~ 18

Author(s):

Andrew K. Heidinger ◽

Istvan Laszlo ◽

Christine C. Molling ◽

Dan Tarpley

Keyword(s):

Surface Temperature ◽

Land Surface Temperature ◽

Land Surface ◽

Single Channel ◽

Weather Prediction ◽

Information Service ◽

Surface Radiation ◽

Cloud Detection ◽

Spectral Shifts ◽

Radiative Transfer Modeling

Abstract Because of spectral shifts from instrument to instrument in the operational NOAA satellite imager longwave infrared channels, the NOAA/National Environmental Satellite, Data, and Information Service (NESDIS) has developed a single-channel land surface temperature (LST) algorithm based on the observed 11-μm radiances, numerical weather prediction data, and radiative transfer modeling that allows for consistent results from the Geostationary Operational Environmental Satellite-I/L (GOES-I/L), GOES-M–P, and Advanced Very High Resolution Radiometer (AVHRR)/1 through 3 sensor versions. This approach is implemented in the real-time NESDIS processing systems [GOES Surface and Insolation Products (GSIP) and Clouds from AVHRR Extended (CLAVR-x)], and in the Pathfinder Atmospheres–Extended (PATMOS-x) climate dataset. An analysis of the PATMOS-x LST against that derived from the upwelling broadband longwave flux at each Surface Radiation Network (SURFRAD) site showed that biases in PATMOS-x were approximately 1 K or less. The standard deviations of the PATMOS-x minus SURFRAD LST biases are generally 2.5 K or less at all sites for all sensors. Using the PATMOS-x minus SURFRAD LST distributions to validate the PATMOS-x cloud detection, the PATMOS-x cloud probability of correct detection values were shown to meet the GOES-R specifications for all sites.

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Evaluation of the ERA5 Sea Surface Skin Temperature with Remotely-Sensed Shipborne Marine-Atmospheric Emitted Radiance Interferometer Data

Remote Sensing ◽

10.3390/rs12111873 ◽

2020 ◽

Vol 12 (11) ◽

pp. 1873 ◽

Cited By ~ 3

Author(s):

Bingkun Luo ◽

Peter J. Minnett

Keyword(s):

Skin Temperature ◽

Pearson Correlation ◽

Weather Prediction ◽

Sea Surface ◽

Sea Temperature ◽

Dust Aerosols ◽

Surface Skin Temperature ◽

Ocean Forecasting ◽

Commercial Applications ◽

Earth Observation Satellites

Sea surface temperature is very important in weather and ocean forecasting, and studying the ocean, atmosphere and climate system. Measuring the sea surface skin temperature (SSTskin) with infrared radiometers onboard earth observation satellites and shipboard instruments is a mature subject spanning several decades. Reanalysis model output SSTskin, such as from the newly released ERA5, is very widely used and has been applied for monitoring climate change, weather prediction research, and other commercial applications. The ERA5 output SSTskin data must be rigorously evaluated to meet the stringent accuracy requirements for climate research. This study aims to estimate the accuracy of the ERA5 SSTskin fields and provide an associated error estimate by using measurements from accurate shipboard infrared radiometers: the Marine-Atmosphere Emitted Radiance Interferometers (M-AERIs). Overall, the ERA5 SSTskin has high correlation with ship-based radiometric measurements, with an average difference of~0.2 K with a Pearson correlation coefficient (R) of 0.993. Parts of the discrepancies are related to dust aerosols and variability in air-sea temperature differences. The downward radiative flux due to dust aerosols leads to significant SSTskin differences for ERA5. The SSTskin differences are greater with the large, positive air–sea temperature differences. This study provides suggestions for the applicability of ERA5 SSTskin fields in a selection of research applications.

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HGPT2: An ERA5-Based Global Model to Estimate Relative Humidity

Remote Sensing ◽

10.3390/rs13112179 ◽

2021 ◽

Vol 13 (11) ◽

pp. 2179

Author(s):

Pedro Mateus ◽

Virgílio B. Mendes ◽

Sandra M. Plecha

Keyword(s):

Water Vapor ◽

Relative Humidity ◽

Real Time ◽

Prediction Models ◽

Weather Prediction ◽

Precipitable Water ◽

Precipitable Water Vapor ◽

Global Navigation Satellite Systems ◽

International Gnss Service ◽

Weighted Mean Temperature

The neutral atmospheric delay is one of the major error sources in Space Geodesy techniques such as Global Navigation Satellite Systems (GNSS), and its modeling for high accuracy applications can be challenging. Improving the modeling of the atmospheric delays (hydrostatic and non-hydrostatic) also leads to a more accurate and precise precipitable water vapor estimation (PWV), mostly in real-time applications, where models play an important role, since numerical weather prediction models cannot be used for real-time processing or forecasting. This study developed an improved version of the Hourly Global Pressure and Temperature (HGPT) model, the HGPT2. It is based on 20 years of ERA5 reanalysis data at full spatial (0.25° × 0.25°) and temporal resolution (1-h). Apart from surface air temperature, surface pressure, zenith hydrostatic delay, and weighted mean temperature, the updated model also provides information regarding the relative humidity, zenith non-hydrostatic delay, and precipitable water vapor. The HGPT2 is based on the time-segmentation concept and uses the annual, semi-annual, and quarterly periodicities to calculate the relative humidity anywhere on the Earth’s surface. Data from 282 moisture sensors located close to GNSS stations during 1 year (2020) were used to assess the model coefficients. The HGPT2 meteorological parameters were used to process 35 GNSS sites belonging to the International GNSS Service (IGS) using the GAMIT/GLOBK software package. Results show a decreased root-mean-square error (RMSE) and bias values relative to the most used zenith delay models, with a significant impact on the height component. The HGPT2 was developed to be applied in the most diverse areas that can significantly benefit from an ERA5 full-resolution model.

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