Monitoring of VIIRS ocean clear-sky brightness temperatures against CRTM simulation in ICVS for TEB/M bands

2017 ◽  
Author(s):  
Ninghai Sun ◽  
Quanhua Liu ◽  
Wenhui Wang ◽  
Bin Zhang ◽  
Fuzhong Weng ◽  
...  
Keyword(s):  
Clear Sky ◽  
Brightness Temperatures ◽  
Sky Brightness

scholarly journals Climatological Diurnal Cycles in Clear-Sky Brightness Temperatures from the High-Resolution Infrared Radiation Sounder (HIRS)

2011 ◽  
Vol 28 (10) ◽  
pp. 1199-1205 ◽  
Author(s):  
Anders V. Lindfors ◽  
Ian A. Mackenzie ◽  
Simon F. B. Tett ◽  
Lei Shi
Keyword(s):  
Monte Carlo ◽  
Fourier Series ◽  
High Resolution ◽  
Infrared Radiation ◽  
Lower Troposphere ◽  
Sampling Bias ◽  
Diurnal Cycles ◽  
Clear Sky ◽  
Brightness Temperatures ◽  
Sky Brightness

Abstract A climatology of the diurnal cycles of HIRS clear-sky brightness temperatures was developed based on measurements over the period 2002–07. This was done by fitting a Fourier series to monthly gridded brightness temperatures of HIRS channels 1–12. The results show a strong land–sea contrast with stronger diurnal cycles over land, and extending from the surface up to HIRS channel 6 or 5, with regional maxima over the subtropics. Over seas, the diurnal cycles are generally small and therefore challenging to detect. A Monte Carlo uncertainty analysis showed that more robust results are reached by aggregating the data zonally before applying the fit. The zonal fits indicate that small diurnal cycles do exist over sea. The results imply that for a long-lived satellite such as NOAA-14, drift in the overpass time can cause a diurnal sampling bias of more than 5 K for channel 8 (surface and lower troposphere).

scholarly journals Worldwide variations in artificial skyglow

2015 ◽  
Vol 5 (1) ◽  
Author(s):  
Christopher C. M. Kyba ◽  
Kai Pong Tong ◽  
Jonathan Bennie ◽  
Ignacio Birriel ◽  
Jennifer J. Birriel ◽  
...  
Keyword(s):  
Monitoring Program ◽  
Artificial Light ◽  
Clear Sky ◽  
International Monitoring ◽  
Sky Brightness ◽  
Ground Truthing ◽  
Study Sites ◽  
Night Sky Brightness ◽  
Key Questions

Abstract Despite constituting a widespread and significant environmental change, understanding of artificial nighttime skyglow is extremely limited. Until now, published monitoring studies have been local or regional in scope and typically of short duration. In this first major international compilation of monitoring data we answer several key questions about skyglow properties. Skyglow is observed to vary over four orders of magnitude, a range hundreds of times larger than was the case before artificial light. Nearly all of the study sites were polluted by artificial light. A non-linear relationship is observed between the sky brightness on clear and overcast nights, with a change in behavior near the rural to urban landuse transition. Overcast skies ranged from a third darker to almost 18 times brighter than clear. Clear sky radiances estimated by the World Atlas of Artificial Night Sky Brightness were found to be overestimated by ~25%; our dataset will play an important role in the calibration and ground truthing of future skyglow models. Most of the brightly lit sites darkened as the night progressed, typically by ~5% per hour. The great variation in skyglow radiance observed from site-to-site and with changing meteorological conditions underlines the need for a long-term international monitoring program.

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 A 1D-Var Approach to Retrieve Clear-Sky Wet Tropospheric Correction from Current and Future Altimetry Missions

2019 ◽  
Vol 36 (3) ◽  
pp. 473-489 ◽  
Author(s):  
Laura Hermozo ◽  
Laurence Eymard ◽  
Fatima Karbou ◽  
Bruno Picard ◽  
Mickael Pardé
Keyword(s):  
Data Assimilation ◽  
Microwave Radiometer ◽  
Passive Microwave ◽  
Simulation Experiments ◽  
Low Frequencies ◽  
Clear Sky ◽  
Brightness Temperatures ◽  
Atmospheric Stratification ◽  
Atmospheric Path ◽  
Occultation Data

AbstractStatistical methods are usually used to provide estimations of the wet tropospheric correction (WTC), necessary to correct altimetry measurements for atmospheric path delays, using brightness temperatures measured at two or three low frequencies from a passive microwave radiometer on board the altimeter mission. Despite their overall accuracy over oceanic surfaces, uncertainties still remain in specific regions of complex atmospheric stratification. Thus, there is still a need to improve the methods currently used by taking into account the frequency-dependent information content of the observations and the atmospheric and surface variations in the surroundings of the observations. In this article we focus on the assimilation of relevant passive microwave observations to retrieve the WTC over ocean using different altimeter mission contexts (current and future, providing brightness temperature measurements at higher frequencies in addition to classical low frequencies). Data assimilation is performed using a one-dimensional variational data assimilation (1D-Var) method. The behavior of the 1D-Var is evaluated by verifying its physical consistency when using pseudo- and real observations. Several observing-system simulation experiments are run and their results are analyzed to evaluate global and regional WTC retrievals. Comparisons of 1D-Var-based TWC retrieval and reference products from classical WTC retrieval algorithms or radio-occultation data are also performed to assess the 1D-Var performances.

scholarly journals Development and Testing of a Clear-Sky Data Selection Algorithm for FY-3C/D Microwave Temperature Sounder-2

2020 ◽  
Vol 12 (9) ◽  
pp. 1478 ◽  
Author(s):  
Zeyi Niu ◽  
Xiaolei Zou ◽  
Peter Sawin Ray
Keyword(s):  
Radial Distance ◽  
Mean Value ◽  
Data Selection ◽  
Selection Algorithm ◽  
Liquid Water Path ◽  
Model Simulations ◽  
Clear Sky ◽  
Brightness Temperatures ◽  
Data Points ◽  
Cloud Liquid Water Path

The Fengyun (FY)-3C/D microwave temperature sounder-2 (MWTS-2) is similar to the Advanced Microwave Sounding Unit-A (AMSU-A), except it lacks two window channels located at 23.8 GHz and 31.4 GHz. This makes a clear-sky data determination challenging for the MWTS-2 due to the unavailability of cloud liquid water path (LWP) retrievable from the two window channels. The purpose of this study is to develop a clear-sky data selection algorithm for the FY-3C/D MWTS-2 based on the bias-removed differences between observations and model simulations of the MWTS-2 50.3-GHz channel 1 (or equivalently AMSU-A channel 3). First, a point is defined as a temporal clear-sky (cloudy) point if the bias-removed difference between observed and simulated brightness temperatures is smaller than or equal to (greater than) 2 K. Then, a temporal clear-sky (cloudy) point is defined as a final clear-sky (cloudy) point if all points within its 60-km (100-km) radial distance are temporal clear-sky (cloudy) points. Finally, if the mean value of the bias-removed differences between observations and simulations in the 100-km circle from a temporal cloudy point are smaller than or equal to (greater than) 2 K, all temporal clear-sky points within this circle are (not) taken as the final clear-sky points. Applications of this algorithm to FY-3C MWTS-2 and MetOp-B AMSU-A lead to the following conclusions: (i) more than 70% (95%) of the clear-sky (cloudy) data points are successfully identified from both AMSU-A and MWTS-2 observations; (ii) the algorithm-selected clear-sky data points were located in clear-sky areas in the GOES-15 imager, and (iii) the bias-removed differences between observations and model simulations of MWTS-2 channel 1 well reveals the eye, the eyewall, and the spiral rainband structure of Super Typhoon Halong (2014).

scholarly journals Clear-Sky Mask for the Advanced Clear-Sky Processor for Oceans

2010 ◽  
Vol 27 (10) ◽  
pp. 1609-1623 ◽  
Author(s):  
B. Petrenko ◽  
A. Ignatov ◽  
Y. Kihai ◽  
A. Heidinger
Keyword(s):  
High Resolution ◽  
Radiative Transfer ◽  
Weather Prediction ◽  
Radiative Transfer Model ◽  
Transfer Model ◽  
Clear Sky ◽  
Brightness Temperatures ◽  
Spatial Uniformity ◽  
Uniformity Test ◽  
Cross Platform

Abstract The Advanced Clear Sky Processor for Oceans (ACSPO) generates clear-sky products, such as SST, clear-sky radiances, and aerosol, from Advanced Very High Resolution Radiometer (AVHRR)-like measurements. The ACSPO clear-sky mask (ACSM) identifies clear-sky pixels within the ACSPO products. This paper describes the ACSM structure and compares the performances of ACSM and its predecessor, Clouds from AVHRR Extended Algorithm (CLAVRx). ACSM essentially employs online clear-sky radiative transfer simulations enabled within ACSPO with the Community Radiative Transfer Model (CRTM) in conjunction with numerical weather prediction atmospheric [Global Forecast System (GFS)] and SST [Reynolds daily high-resolution blended SST (DSST)] fields. The baseline ACSM tests verify the accuracy of fitting observed brightness temperatures with CRTM, check retrieved SST for consistency with Reynolds SST, and identify ambient cloudiness at the boundaries of cloudy systems. Residual cloud effects are screened out with several tests, adopted from CLAVRx, and with the SST spatial uniformity test designed to minimize misclassification of sharp SST gradients as clouds. Cross-platform and temporal consistencies of retrieved SSTs are maintained by accounting for SST and brightness temperature biases, estimated within ACSPO online and independently from ACSM. The performance of ACSM is characterized in terms of statistics of deviations of retrieved SST from the DSST. ACSM increases the amount of “clear” pixels by 30% to 40% and improves statistics of retrieved SST compared with CLAVRx. ACSM is also shown to be capable of producing satisfactory statistics of SST anomalies if the reference SST field for the exact date of observations is unavailable at the time of processing.

scholarly journals Climate Model–Simulated Diurnal Cycles in HIRS Clear-Sky Brightness Temperatures

2012 ◽  
Vol 25 (17) ◽  
pp. 5845-5863 ◽  
Author(s):  
Ian A. MacKenzie ◽  
Simon F. B. Tett ◽  
Anders V. Lindfors
Keyword(s):  
Water Vapor ◽  
Brightness Temperature ◽  
Climate Models ◽  
Tropospheric Temperature ◽  
Diurnal Variability ◽  
Diurnal Cycles ◽  
Clear Sky ◽  
Recent Climate ◽  
Diurnal Behavior ◽  
Sky Brightness

Abstract Clear-sky brightness temperature measurements from the High-Resolution Infrared Radiation Sounder (HIRS) are simulated with two climate models via a radiative transfer code. The models are sampled along the HIRS orbit paths to derive diurnal climatologies of simulated brightness temperature analogous to an existing climatology based on HIRS observations. Simulated and observed climatologies are compared to assess model performance and the robustness of the observed climatology. Over land, there is good agreement between simulations and observations, with particularly high consistency for the tropospheric temperature channels. Diurnal cycles in the middle- and upper-tropospheric water vapor channels are weak in both simulations and observations, but the simulated diurnal brightness temperature ranges are smaller than are observed with different phase and there are also intermodel differences. Over sea, the absence of diurnal variability in the models’ sea surface temperatures causes an underestimate of the small diurnal cycles measured in the troposphere. The simulated and observed climatologies imply similar diurnal sampling biases in the HIRS record for the tropospheric temperature channels, but for the upper-tropospheric water vapor channel, differences in the contributions of the 24- and 12-hourly diurnal harmonics lead to differences in the implied bias. Comparison of diurnal cycles derived from HIRS-like and full model sampling suggests that the HIRS measurements are sufficient to fully constrain the diurnal behavior. Overall, the results suggest that recent climate models well represent the major processes driving the diurnal behavior of clear-sky brightness temperature in the HIRS channels. This encourages further studies of observed and simulated climate trends over the HIRS era.

scholarly journals Sky brightness measurements for different environmental conditions by the example of St. Petersburg

2018 ◽  
Vol 43 ◽  
pp. 01010
Author(s):  
Svetlana Kolgushkina
Keyword(s):  
Environmental Conditions ◽  
Light Emission ◽  
Anthropogenic Factors ◽  
City Center ◽  
Clear Sky ◽  
Series Of Experiments ◽  
Sky Brightness ◽  
Sky Conditions ◽  
The City ◽  
Night Sky Brightness

St. Petersburg is unique place in Russia with an environmental phenomenon called “white nights” during summertime: sky brightness levels are influenced mostly by environmental conditions. Sky conditions during the winter are opposite: light emission is mostly caused by the anthropogenic factors. A series of experiments were conducted between May and December 2017 using a Sky Quality Meter (SQM-LU-DL), a night sky brightness photometer, to understand the differences between sky brightness levels for different environmental conditions and seasonal variations. Sky brightness distinction between the city center and 20 km distance were estimated for clear sky conditions.

scholarly journals Biases Characteristics Assessment of the Advanced Geosynchronous Radiation Imager (AGRI) Measurement on Board Fengyun–4A Geostationary Satellite

2020 ◽  
Vol 12 (18) ◽  
pp. 2871
Author(s):  
Jia Zhu ◽  
Jiong Shu ◽  
Wei Guo
Keyword(s):  
Data Assimilation ◽  
Near Infrared ◽  
Weather Forecasting ◽  
Weather Prediction ◽  
Infrared Observation ◽  
Infrared Observations ◽  
Clear Sky ◽  
Brightness Temperatures ◽  
Geostationary Meteorological Satellite ◽  
Regression Tests

The Chinese Fengyun–4A geostationary meteorological satellite was successfully launched on 11 December 2016, carrying an Advanced Geostationary Radiation Imager (AGRI) to provide the observations of visible, near infrared, and infrared bands with improved spectral, spatial, and temporal resolution. The AGRI infrared observations can be assimilated into numerical weather prediction (NWP) data assimilation systems to improve the atmospheric analysis and weather forecasting capabilities. To achieve data assimilation, the first and crucial step is to characterize and evaluate the biases of the AGRI brightness temperatures in infrared channels 8–14. This study conducts the assessment of clear–sky AGRI full–disk infrared observation biases by coupling the RTTOV model and ERA Interim analysis. The AGRI observations are generally in good agreement with the model simulations. It is found that the biases over the ocean and land are less than 1.4 and 1.6 K, respectively. For bias difference between land and ocean, channels 11–13 are more obvious than water vapor channels 9–10. The fitting coefficient of linear regression tests between AGRI biases and sensor zenith angles manifests no obvious scan angle–dependent biases over ocean. All infrared channels observations are scene temperature–dependent over the ocean and land.

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