scholarly journals Measuring Cloud Cover and Brightness Temperature with a Ground-Based Thermal Infrared Camera

2008 ◽  
Vol 47 (2) ◽  
pp. 683-693 ◽  
Author(s):  
Stephen Smith ◽  
Ralf Toumi
Keyword(s):  
Brightness Temperature ◽  
Cloud Cover ◽  
Infrared Camera ◽  
Thermal Infrared ◽  
Cloud Base ◽  
Clear Sky ◽  
Brightness Temperatures ◽  
External Data ◽  
Infrared Cameras ◽  
Two Parameters

Abstract Thermal infrared cameras can be used to monitor clouds and the sky at high spatial and temporal resolutions. In particular, this study shows that, without the need for any external data, cloud cover can be retrieved both day and night over a field of view extending to zenith angles of ∼80°. Zenith clear sky temperatures are estimated for cloud cover up to 80%. During periods of 50% cloud cover or more the cloud-base brightness temperatures (CBBTs) can be calculated to an accuracy of ±1 K. These calculations are made possible by using a new parameterization for the variation of sky brightness temperature with zenith angle. Both clear and cloudy conditions are found to follow this simple empirical equation more closely than the widely used parameterization of Unsworth and Monteith. A simple, angle-dependent threshold system based on cloud transmittance can then be used to retrieve cloud cover, and clear sky temperature and CBBT are calculated using the two parameters resulting from the fitting process.

scholarly journals Version 4 CALIPSO IIR ice and liquid water cloud microphysical properties, Part I: the retrieval algorithms

2020 ◽  
Author(s):  
Anne Garnier ◽  
Jacques Pelon ◽  
Nicolas Pascal ◽  
Mark A. Vaughan ◽  
Philippe Dubuisson ◽  
...  
Keyword(s):  
Liquid Water ◽  
Cloud Base ◽  
Transfer Model ◽  
Effective Diameter ◽  
Ice Clouds ◽  
Clear Sky ◽  
Brightness Temperatures ◽  
Microphysical Properties ◽  
Data Products ◽  
Level 2

Abstract. Following the release of the Version 4 Cloud-Aerosol Lidar with Orthogonal Polarization (CALIOP) data products from the Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observations (CALIPSO) mission, a new version 4 (V4) of the CALIPSO Imaging Infrared Radiometer (IIR) Level 2 data products has been developed. The IIR Level 2 data products include cloud effective emissivities and cloud microphysical properties such as effective diameter and ice or liquid water path estimates. Dedicated retrievals for water clouds were added in V4, taking advantage of the high sensitivity of the IIR retrieval technique to small particle sizes. This paper (Part I) describes the improvements in the V4 algorithms compared to those used in the version 3 (V3) release, while results will be presented in a companion (Part II) paper. To reduce biases at very small emissivities that were made evident in V3, the radiative transfer model used to compute clear sky brightness temperatures over oceans has been updated and tuned for the simulations using MERRA-2 data to match IIR observations in clear sky conditions. Furthermore, the clear-sky mask has been refined compared to V3 by taking advantage of additional information now available in the V4 CALIOP 5-km layer products used as an input to the IIR algorithm. After sea surface emissivity adjustments, observed and computed brightness temperatures differ by less than ± 0.2 K at night for the three IIR channels centered at 08.65, 10.6, and 12.05 µm, and inter-channel biases are reduced from several tens of Kelvin in V3 to less than 0.1 K in V4. We have also aimed at improving retrievals in ice clouds having large optical depths by refining the determination of the radiative temperature needed for emissivity computation. The initial V3 estimate, namely the cloud centroid temperature derived from CALIOP, is corrected using a parameterized function of temperature difference between cloud base and top altitudes, cloud absorption optical depth, and the CALIOP multiple scattering correction factor. As shown in Part II, this improvement reduces the low biases at large optical depths that were seen in V3, and increases the number of retrievals in dense ice clouds. As in V3, the IIR microphysical retrievals use the concept of microphysical indices applied to the pairs of IIR channels at 12.05 μm and 10.6 μm and at 12.05 μm and 08.65 μm. The V4 algorithm uses ice look-up tables (LUTs) built using two ice crystal models from the recent TAMUice 2016 database, namely the single hexagonal column model and the 8-element column aggregate model, from which bulk properties are synthesized using a gamma size distribution. Four sets of effective diameters derived from a second approach are also reported in V4. Here, the LUTs are analytical functions relating microphysical index applied to IIR channels 12.05 µm and 10.6 µm and effective diameter as derived from in situ measurements at tropical and mid-latitudes during the TC4 and SPARTICUS field experiments.

scholarly journals Evaluating solar radiation forecast uncertainty

2018 ◽  
Author(s):  
Minttu Tuononen ◽  
Ewan J. O'Connor ◽  
Victoria A. Sinclair
Keyword(s):  
Solar Radiation ◽  
Cloud Cover ◽  
Spatial Scales ◽  
Forecast Error ◽  
Surface Radiation ◽  
Cloud Base ◽  
Strong Impact ◽  
Positive Bias ◽  
Radiative Balance ◽  
Clear Sky

Abstract. The presence of clouds, and their characteristics, has a strong impact on the radiative balance of the Earth and on the amount of solar radiation reaching the Earth's surface. Many applications require accurate forecasts of surface radiation on weather timescales, for example, solar energy and UV radiation forecasts. Here we investigate how operational forecasts of low and mid-level clouds affect the accuracy of solar radiation forecasts. Four years of cloud and solar radiation observations from one site – Helsinki, Finland, are analysed. Cloud observations are obtained from a ceilometer and therefore, we first develop algorithms to reliably detect cloud base, precipitation and fog. These new algorithms are widely applicable for both operational use and research, such as in-cloud icing detection for the wind energy industry and for aviation. The cloud and radiation observations are compared to forecasts from the Integrated Forecast System (IFS) run operationally and developed by the European Centre for Medium-Range Weather Forecasts (ECMWF). We develop methods to evaluate the skill of the cloud and radiation forecasts. These methods can potentially be extended to hundreds of sites globally. Over Helsinki, the measured Global Horizontal Irradiance (GHI) is strongly influenced by its northerly location and the annual variation in cloudiness. Solar radiation forecast error is therefore larger in summer than in winter, but the relative error in the solar radiation forecast is more or less constant throughout the year. The mean overall bias in the GHI forecast is positive (8 W m−2). The observed and forecast distributions in cloud cover, at the spatial scales we are considering, are strongly skewed towards clear-sky and overcast situations. Cloud cover forecasts show more skill in winter when the cloud cover is predominantly overcast; in summer there are more clear-sky and broken cloud situations. A negative bias was found in forecast GHI for correctly forecast clear-sky cases and a positive bias in correctly forecast overcast cases. Temporal averaging improved the cloud cover forecast and hence decreased the solar radiation forecast error, but made little impact on the overall bias. The positive bias seen in overcast situations occurs when the model cloud has low values of liquid water path (LWP). We attribute this bias to the model having LWP values that are too low or that the model optical properties for clouds with low LWP are incorrect.

scholarly journals HIRS channel 12 brightness temperature dataset and its correlations with major climate indices

2013 ◽  
Vol 13 (14) ◽  
pp. 6907-6920 ◽  
Author(s):  
L. Shi ◽  
C. J. Schreck III ◽  
V. O. John
Keyword(s):  
Water Vapor ◽  
Brightness Temperature ◽  
Regional Climate ◽  
Climate Indices ◽  
Teleconnection Patterns ◽  
Clear Sky ◽  
Vapor Channel ◽  
Brightness Temperatures ◽  
Spatial Coverage ◽  
The Tropics

Abstract. A new version of the High-Resolution Infrared Radiation Sounder (HIRS) upper tropospheric water vapor channel (channel 12) brightness temperature dataset is developed using intersatellite calibrated data. In this dataset, only those pixels affected by upper tropospheric clouds are discarded. Compared to the previous version that was based on column-clear-sky data, the new version has much better daily spatial coverage. The HIRS observation patterns are compared to microwave sounder measurements. The differences between the two types of sounders vary with respect to brightness temperature with larger differences for higher (dry) values. Correlations between the HIRS upper tropospheric water vapor channel brightness temperatures and several major climate indices show strong signals during cold seasons. The selected climate indices track climate variation signals covering regions from the tropics to the poles. Qualitatively, moist signals are correlated with troughs and ascending branches of the circulation, while dry signals occur with ridges and descent. These correlations show the potential of using the upper tropospheric water vapor channel brightness temperature dataset together with a suite of many atmospheric variables to monitor regional climate changes and locate global teleconnection patterns.

scholarly journals Evaluating solar radiation forecast uncertainty

2019 ◽  
Vol 19 (3) ◽  
pp. 1985-2000 ◽  
Author(s):  
Minttu Tuononen ◽  
Ewan J. O'Connor ◽  
Victoria A. Sinclair
Keyword(s):  
Solar Radiation ◽  
Cloud Cover ◽  
Spatial Scales ◽  
Forecast Error ◽  
Surface Radiation ◽  
Cloud Base ◽  
Strong Impact ◽  
Positive Bias ◽  
Radiative Balance ◽  
Clear Sky

Abstract. The presence of clouds and their characteristics have a strong impact on the radiative balance of the Earth and on the amount of solar radiation reaching the Earth's surface. Many applications require accurate forecasts of surface radiation on weather timescales, for example solar energy and UV radiation forecasts. Here we investigate how operational forecasts of low and mid-level clouds affect the accuracy of solar radiation forecasts. A total of 4 years of cloud and solar radiation observations from one site in Helsinki, Finland, are analysed. Cloud observations are obtained from a ceilometer and therefore we first develop algorithms to reliably detect cloud base, precipitation, and fog. These new algorithms are widely applicable for both operational use and research, such as in-cloud icing detection for the wind energy industry and for aviation. The cloud and radiation observations are compared to forecasts from the Integrated Forecast System (IFS) run operationally and developed by the European Centre for Medium-Range Weather Forecasts (ECMWF). We develop methods to evaluate the skill of the cloud and radiation forecasts. These methods can potentially be extended to hundreds of sites globally. Over Helsinki, the measured global horizontal irradiance (GHI) is strongly influenced by its northerly location and the annual variation in cloudiness. Solar radiation forecast error is therefore larger in summer than in winter, but the relative error in the solar radiation forecast is more or less constant throughout the year. The mean overall bias in the GHI forecast is positive (8 W m−2). The observed and forecast distributions in cloud cover, at the spatial scales we are considering, are strongly skewed towards clear-sky and overcast situations. Cloud cover forecasts show more skill in winter when the cloud cover is predominantly overcast; in summer there are more clear-sky and broken cloud situations. A negative bias was found in forecast GHI for correctly forecast clear-sky cases and a positive bias in correctly forecast overcast cases. Temporal averaging improved the cloud cover forecast and hence decreased the solar radiation forecast error. The positive bias seen in overcast situations occurs when the model cloud has low values of liquid water path (LWP). We attribute this bias to the model having LWP values that are too low or the model optical properties for clouds with low LWP being incorrect.

scholarly journals Version 4 CALIPSO Imaging Infrared Radiometer ice and liquid water cloud microphysical properties – Part I: The retrieval algorithms

2021 ◽  
Vol 14 (5) ◽  
pp. 3253-3276 ◽  
Author(s):  
Anne Garnier ◽  
Jacques Pelon ◽  
Nicolas Pascal ◽  
Mark A. Vaughan ◽  
Philippe Dubuisson ◽  
...  
Keyword(s):  
Liquid Water ◽  
Cloud Base ◽  
Transfer Model ◽  
Effective Diameter ◽  
Clear Sky ◽  
Brightness Temperatures ◽  
Microphysical Properties ◽  
Data Products ◽  
Level 2 ◽  
Infrared Radiometer

Abstract. Following the release of the version 4 Cloud-Aerosol Lidar with Orthogonal Polarization (CALIOP) data products from the Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observations (CALIPSO) mission, a new version (version 4; V4) of the CALIPSO Imaging Infrared Radiometer (IIR) Level 2 data products has been developed. The IIR Level 2 data products include cloud effective emissivities and cloud microphysical properties such as effective diameter and ice or liquid water path estimates. Dedicated retrievals for water clouds were added in V4, taking advantage of the high sensitivity of the IIR retrieval technique to small particle sizes. This paper (Part I) describes the improvements in the V4 algorithms compared to those used in the version 3 (V3) release, while results will be presented in a companion (Part II) paper. The IIR Level 2 algorithm has been modified in the V4 data release to improve the accuracy of the retrievals in clouds of very small (close to 0) and very large (close to 1) effective emissivities. To reduce biases at very small emissivities that were made evident in V3, the radiative transfer model used to compute clear-sky brightness temperatures over oceans has been updated and tuned for the simulations using Modern-Era Retrospective analysis for Research and Applications version 2 (MERRA-2) data to match IIR observations in clear-sky conditions. Furthermore, the clear-sky mask has been refined compared to V3 by taking advantage of additional information now available in the V4 CALIOP 5 km layer products used as an input to the IIR algorithm. After sea surface emissivity adjustments, observed and computed brightness temperatures differ by less than ±0.2 K at night for the three IIR channels centered at 08.65, 10.6, and 12.05 µm, and inter-channel biases are reduced from several tens of Kelvin in V3 to less than 0.1 K in V4. We have also improved retrievals in ice clouds having large emissivity by refining the determination of the radiative temperature needed for emissivity computation. The initial V3 estimate, namely the cloud centroid temperature derived from CALIOP, is corrected using a parameterized function of temperature difference between cloud base and top altitudes, cloud absorption optical depth, and CALIOP multiple scattering correction factor. As shown in Part II, this improvement reduces the low biases at large optical depths that were seen in V3 and increases the number of retrievals. As in V3, the IIR microphysical retrievals use the concept of microphysical indices applied to the pairs of IIR channels at 12.05 and 10.6 µm and at 12.05 and 08.65 µm. The V4 algorithm uses ice look-up tables (LUTs) built using two ice habit models from the recent “TAMUice2016” database, namely the single-hexagonal-column model and the eight-element column aggregate model, from which bulk properties are synthesized using a gamma size distribution. Four sets of effective diameters derived from a second approach are also reported in V4. Here, the LUTs are analytical functions relating microphysical index applied to IIR channels 12.05 and 10.6 µm and effective diameter as derived from in situ measurements at tropical and midlatitudes during the Tropical Composition, Cloud, and Climate Coupling (TC4) and Small Particles in Cirrus Science and Operations Plan (SPARTICUS) field experiments.

scholarly journals Assessment of Cracking Widths in a Concrete Wall Based on TIR Radiances of Cracking

Sensors ◽  
2020 ◽  
Vol 20 (17) ◽  
pp. 4980
Author(s):  
Tung-Ching Su
Keyword(s):  
Crack Detection ◽  
Thermal Infrared ◽  
Subtropical Climate ◽  
Architectural Style ◽  
Concrete Wall ◽  
Concrete Crack ◽  
Operation Procedure ◽  
Historic House ◽  
Multiple Measurements ◽  
Infrared Cameras

The techniques of concrete crack detection, as well as assessments based on thermography coupled with ultrasound, have been presented in many works; however, they have generally needed an additional source of thermal infrared (TIR) radiance and have only been applied in laboratories. Considering the accessibility of thermal infrared cameras, a TIR camera (NEC F30W) was employed to detect cracking in the concrete wall of an historic house with a western architectural style in Kinmen, Taiwan, based on the TIR radiances of cracking. An operation procedure involving a series of image processing and statistical analysis processes was designed to evaluate the performance of the TIR camera in the assessment of the cracking width. This procedure using multiple measurements was implemented from March to August 2019, and the t-tests indicated that the temperature differences between the inside and outline of the concrete cracks remained insignificant as the temperature or relative humidity (RH) in the subtropical climate rose. The experimental results of the operation procedure indicated that the maximum focusing range, which is related to the size of the sensor array, and the minimum detectable crack width of a TIR camera should be 1.0 m and 6.0 mm, respectively, in order to derive a linear regression model with a determination coefficient R2 of 0.733 to estimate the cracking widths, based on the temperature gradients. The validation results showed that there was an approximate R2 value of 0.8 and a total root mean square error of ±2.5 mm between the cracking width estimations and the observations.

scholarly journals Mind the gap – Part 1: Accurately locating warm marine boundary layer clouds and precipitation using spaceborne radars

2020 ◽  
Vol 13 (5) ◽  
pp. 2363-2379 ◽  
Author(s):  
Katia Lamer ◽  
Pavlos Kollias ◽  
Alessandro Battaglia ◽  
Simon Preval
Keyword(s):  
Boundary Layer ◽  
Cloud Cover ◽  
Marine Boundary Layer ◽  
Short Pulse ◽  
Pulse Mode ◽  
Cloud Base ◽  
Boundary Layer Clouds ◽  
Highly Sensitive ◽  
Eastern North Atlantic ◽  
Long Pulse

Abstract. Ground-based radar observations show that, over the eastern North Atlantic, 50 % of warm marine boundary layer (WMBL) hydrometeors occur below 1.2 km and have reflectivities of < −17 dBZ, thus making their detection from space susceptible to the extent of surface clutter and radar sensitivity. Surface clutter limits the ability of the CloudSat cloud profiling radar (CPR) to observe the true cloud base in ∼52 % of the cloudy columns it detects and true virga base in ∼80 %, meaning the CloudSat CPR often provides an incomplete view of even the clouds it does detect. Using forward simulations, we determine that a 250 m resolution radar would most accurately capture the boundaries of WMBL clouds and precipitation; that being said, because of sensitivity limitations, such a radar would suffer from cloud cover biases similar to those of the CloudSat CPR. Observations and forward simulations indicate that the CloudSat CPR fails to detect 29 %–43 % of the cloudy columns detected by ground-based sensors. Out of all configurations tested, the 7 dB more sensitive EarthCARE CPR performs best (only missing 9.0 % of cloudy columns) indicating that improving radar sensitivity is more important than decreasing the vertical extent of surface clutter for measuring cloud cover. However, because 50 % of WMBL systems are thinner than 400 m, they tend to be artificially stretched by long sensitive radar pulses, hence the EarthCARE CPR overestimation of cloud top height and hydrometeor fraction. Thus, it is recommended that the next generation of space-borne radars targeting WMBL science should operate interlaced pulse modes including both a highly sensitive long-pulse mode and a less sensitive but clutter-limiting short-pulse mode.

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