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25 pages, 3812 KiB

Open AccessArticle

Accuracy of Vaisala RS41 and RS92 Upper Tropospheric Humidity Compared to Satellite Hyperspectral Infrared Measurements

by Bomin Sun, Xavier Calbet, Anthony Reale, Steven Schroeder, Manik Bali, Ryan Smith and Michael Pettey

Remote Sens. 2021, 13(2), 173; https://doi.org/10.3390/rs13020173 - 6 Jan 2021

Cited by 10 | Viewed by 2679

Abstract

Radiosondes are important for calibrating satellite sensors and assessing sounding retrievals. Vaisala RS41 radiosondes have mostly replaced RS92 in the Global Climate Observing System (GCOS) Reference Upper Air Network (GRUAN) and the conventional network. This study assesses RS41 and RS92 upper tropospheric humidity [...] Read more.

Radiosondes are important for calibrating satellite sensors and assessing sounding retrievals. Vaisala RS41 radiosondes have mostly replaced RS92 in the Global Climate Observing System (GCOS) Reference Upper Air Network (GRUAN) and the conventional network. This study assesses RS41 and RS92 upper tropospheric humidity (UTH) accuracy by comparing with Infrared Atmospheric Sounding Interferometer (IASI) upper tropospheric water vapor absorption spectrum measurements. Using single RS41 and RS92 soundings at three GRUAN and DOE Atmospheric Radiation Measurement (ARM) sites and dual RS92/RS41 launches at three additional GRUAN sites, collocated with cloud-free IASI radiances (OBS), we compute Line-by-Line Radiative Transfer Model radiances for radiosonde profiles (CAL). We analyze OBS-CAL differences from 2015 to 2020, for daytime, nighttime, and dusk/dawn separately if data is available, for standard (STD) RS92 and RS41 processing, and RS92 GRUAN Data Processing (GDP; RS41 GDP is in development). We find that daytime RS41 (even without GDP) has ~1% smaller UTH errors than GDP RS92. RS41 may still have a dry bias of 1–1.5% for both daytime and nighttime, and a similar error for nighttime RS92 GDP, while standard RS92 may have a dry bias of 3–4%. These sonde humidity biases are probably upper limits since “cloud-free” scenes could still be cloud contaminated. Radiances computed from European Centre for Medium-Range Weather Forecasts (ECMWF) analyses match better than radiosondes with IASI measurements, perhaps because ECMWF assimilates IASI measurements. Relative differences between RS41 STD and RS92 GDP, or between radiosondes and ECMWF humidity profiles obtained from the radiance analysis, are consistent with their differences obtained directly from the RH measurements. Full article

(This article belongs to the Special Issue Satellite Data Application, Validation and Calibration for Atmospheric Observation)

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19 pages, 6353 KiB

Open AccessArticle

Applying Deep Learning to Clear-Sky Radiance Simulation for VIIRS with Community Radiative Transfer Model—Part 2: Model Architecture and Assessment

by Xingming Liang and Quanhua (Mark) Liu

Remote Sens. 2020, 12(22), 3825; https://doi.org/10.3390/rs12223825 - 21 Nov 2020

Cited by 10 | Viewed by 3205

Abstract

A fully connected “deep” neural network algorithm with the Community Radiative Transfer Model (FCDN_CRTM) is proposed to explore the efficiency and accuracy of reproducing the Visible Infrared Imaging Radiometer Suite (VIIRS) radiances in five thermal emission M (TEB/M) bands. The model was trained [...] Read more.

A fully connected “deep” neural network algorithm with the Community Radiative Transfer Model (FCDN_CRTM) is proposed to explore the efficiency and accuracy of reproducing the Visible Infrared Imaging Radiometer Suite (VIIRS) radiances in five thermal emission M (TEB/M) bands. The model was trained and tested in the nighttime global ocean clear-sky domain, in which the VIIRS observation minus CRTM (O-M) biases have been well validated in recent years. The atmosphere profile from the European Centre for Medium-Range Weather Forecasts (ECMWF) and sea surface temperature (SST) from the Canadian Meteorology Centre (CMC) were used as FCDN_CRTM input, and the CRTM-simulated brightness temperatures (BTs) were defined as labels. Six dispersion days’ data from 2019 to 2020 were selected to train the FCDN_CRTM, and the clear-sky pixels were identified by an enhanced FCDN clear-sky mask (FCDN_CSM) model, which was demonstrated in Part 1. The trained model was then employed to predict CRTM BTs, which were further validated with the CRTM BTs and the VIIRS sensor data record (SDR) for both efficiency and accuracy. With iterative refinement of the model design and careful treatment of the input data, the agreement between the FCDN_CRTM and the CRTM was generally good, including the satellite zenith angle and column water vapor dependencies. The mean biases of the FCDN_CRTM minus CRTM (F-C) were typically ~0.01 K for all five bands, and the high accuracy persisted during the whole analysis period. Moreover, the standard deviations (STDs) were generally less than 0.1 K and were consistent for approximately half a year, before they significantly degraded. The validation with VIIRS SDR data revealed that both the predicted mean biases and the STD of the VIIRS observation minus FCDN_CRTM (V-F) were comparable with the VIIRS minus direct CRTM simulation (V-C). Meanwhile, both V-F and V-C exhibited consistent global geophysical and statistical distribution, as well as stable long-term performance. Furthermore, the FCDN_CRTM processing time was more than 40 times faster than CRTM simulation. The highly efficient, accurate, and stable performances indicate that the FCDN_CRTM is a potential solution for global and real-time monitoring of sensor observation minus model simulation, particularly for high-resolution sensors. Full article

(This article belongs to the Special Issue Satellite Data Application, Validation and Calibration for Atmospheric Observation)

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20 pages, 4559 KiB

Open AccessArticle

Tropical Cyclone Climatology from Satellite Passive Microwave Measurements

by Song Yang, Richard Bankert and Joshua Cossuth

Remote Sens. 2020, 12(21), 3610; https://doi.org/10.3390/rs12213610 - 3 Nov 2020

Cited by 9 | Viewed by 3047

Abstract

The satellite passive microwave (PMW) sensor brightness temperatures (TBs) of all tropical cyclones (TCs) from 1987–2012 have been carefully calibrated for inter-sensor frequency differences, center position fixing using the Automated Rotational Center Hurricane Eye Retrieval (ARCHER) scheme, and application of the Backus–Gilbert interpolation [...] Read more.

The satellite passive microwave (PMW) sensor brightness temperatures (TBs) of all tropical cyclones (TCs) from 1987–2012 have been carefully calibrated for inter-sensor frequency differences, center position fixing using the Automated Rotational Center Hurricane Eye Retrieval (ARCHER) scheme, and application of the Backus–Gilbert interpolation scheme for better presentation of the TC horizontal structure. With additional storm motion direction and the 200–850 hPa wind shear direction, a unique and comprehensive TC database is created for this study. A reliable and detailed climatology for each TC category is analyzed and discussed. There is significant annual variability of the number of storms at hurricane intensity, but the annual number of all storms is relatively stable. Results based on the analysis of the 89 GHz horizontal polarization TBs over oceans are presented in this study. An eyewall contraction is clearly displayed with an increase in TC intensity. Three composition schemes are applied to present a reliable and detailed TC climatology at each intensity category and its geographic characteristics. The global composition relative to the North direction is not able to lead a realistic structure for an individual TC. Enhanced convection in the down-motion quadrants relative to direction of TC motion is obvious for Cat 1–3 TCs, while Cat 4–5 TCs still have a concentric pattern of convection within 200 km radius. Regional differences are evident for weak storms. Results indicate the direction of TC movement has more impact on weak storms than on Cat 4–5 TCs. A striking feature is that all TCs have a consistent pattern of minimum TBs at 89 GHz in the downshear left quadrant (DSLQ) for the northern hemisphere basins and in the downshear right quadrant (DSRQ) for the southern hemisphere basin, regarding the direction of the 200–850 hPa wind shear. Tropical depression and tropical storm have the minimum TBs in the downshear quadrants. The axis of the minimum TBs is slightly shifted toward the vertical shear direction. There is no geographic variation of storm structure relative to the vertical wind shear direction except over the southern hemisphere which shows a mirror image of the storm structure over the northern hemisphere. This study indicates that regional variation of storm structure relative to storm motion direction is mainly due to differences of the vertical wind shear direction among these basins. Results demonstrate the direction of the 200–850 hPa wind shear plays a critical role in TC structure. Full article

(This article belongs to the Special Issue Satellite Data Application, Validation and Calibration for Atmospheric Observation)

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30 pages, 13345 KiB

Open AccessArticle

Gridded Satellite Sounding Retrievals in Operational Weather Forecasting: Product Description and Emerging Applications

by Emily Berndt, Nadia Smith, Jason Burks, Kris White, Rebekah Esmaili, Arunas Kuciauskas, Erika Duran, Roger Allen, Frank LaFontaine and Jeff Szkodzinski

Remote Sens. 2020, 12(20), 3311; https://doi.org/10.3390/rs12203311 - 12 Oct 2020

Cited by 20 | Viewed by 4445

Abstract

The National Aeronautics and Space Administration (NASA) Short-term Prediction Research and Transition Center (SPoRT) has been part of a collaborative effort within the National Oceanic and Atmospheric Administration (NOAA) Joint Polar Satellite System (JPSS) Proving Ground and Risk Reduction (PGRR) Program to develop [...] Read more.

The National Aeronautics and Space Administration (NASA) Short-term Prediction Research and Transition Center (SPoRT) has been part of a collaborative effort within the National Oceanic and Atmospheric Administration (NOAA) Joint Polar Satellite System (JPSS) Proving Ground and Risk Reduction (PGRR) Program to develop gridded satellite sounding retrievals for the operational weather forecasting community. The NOAA Unique Combined Atmospheric Processing System (NUCAPS) retrieves vertical profiles of temperature, water vapor, trace gases, and cloud properties derived from infrared and microwave sounder measurements. A new, optimized method for deriving NUCAPS level 2 horizontally and vertically gridded products is described here. This work represents the development of approaches to better synthesize remote sensing observations that ultimately increase the availability and usability of NUCAPS observations. This approach, known as “Gridded NUCAPS”, was developed to more effectively visualize NUCAPS observations to aid in the quick identification of thermodynamic spatial gradients. Gridded NUCAPS development was based on operations-to-research feedback and is now part of the operational National Weather Service display system. In this paper, we discuss how Gridded NUCAPS was designed, how relevant atmospheric fields are derived, its operational application in pre-convective weather forecasting, and several emerging applications that expand the utility of NUCAPS for monitoring phenomena such as fire weather, the Saharan Air Layer, and stratospheric air intrusions. Full article

(This article belongs to the Special Issue Satellite Data Application, Validation and Calibration for Atmospheric Observation)

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20 pages, 13975 KiB

Open AccessArticle

Comparison of SLSTR Thermal Emissive Bands Clear-Sky Measurements with Those of Geostationary Imagers

by Bingkun Luo and Peter J. Minnett

Remote Sens. 2020, 12(20), 3279; https://doi.org/10.3390/rs12203279 - 9 Oct 2020

Cited by 1 | Viewed by 2557

Abstract

The Sentinel-3 series satellites belong to the European Earth Observation satellite missions for supporting oceanography, land, and atmospheric studies. The Sea and Land Surface Temperature Radiometer (SLSTR) onboard the Sentinel-3 satellites was designed to provide a significant improvement in remote sensing of skin [...] Read more.

The Sentinel-3 series satellites belong to the European Earth Observation satellite missions for supporting oceanography, land, and atmospheric studies. The Sea and Land Surface Temperature Radiometer (SLSTR) onboard the Sentinel-3 satellites was designed to provide a significant improvement in remote sensing of skin sea surface temperature (SST_skin). The successful application of SLSTR-derived SST_skin fields depends on their accuracies. Based on sensor-dependent radiative transfer model simulations, geostationary Geostationary Operational Environmental Satellite (GOES-16) Advanced Baseline Imagers (ABI) and Meteosat Second Generation (MSG-4) Spinning Enhanced Visible and Infrared Imager (SEVIRI) brightness temperatures (BT) have been transformed to SLSTR equivalents to permit comparisons at the pixel level in three ocean regions. The results show the averaged BT differences are on the order of 0.1 K and the existence of small biases between them are likely due to the uncertainties in cloud masking, satellite view angle, solar azimuth angle, and reflected solar light. This study demonstrates the feasibility of combining SST_skin retrievals from SLSTR with those of ABI and SEVIRI. Full article

(This article belongs to the Special Issue Satellite Data Application, Validation and Calibration for Atmospheric Observation)

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23 pages, 4677 KiB

Open AccessArticle

Evaluating the Magnitude of VIIRS Out-of-Band Response for Varying Earth Spectra

by Benjamin Scarino, David R. Doelling, Rajendra Bhatt, Arun Gopalan and Conor Haney

Remote Sens. 2020, 12(19), 3267; https://doi.org/10.3390/rs12193267 - 8 Oct 2020

Viewed by 2875

Abstract

Prior evaluations of Visible Infrared Imaging Radiometer Suite (VIIRS) out-of-band (OOB) contribution to total signal revealed specification exceedance for multiple key solar reflective and infrared bands that are of interest to the passive remote-sensing community. These assessments are based on laboratory measurements, and [...] Read more.

Prior evaluations of Visible Infrared Imaging Radiometer Suite (VIIRS) out-of-band (OOB) contribution to total signal revealed specification exceedance for multiple key solar reflective and infrared bands that are of interest to the passive remote-sensing community. These assessments are based on laboratory measurements, and although highly useful, do not necessarily translate to OOB contribution with consideration of true Earth-reflected or Earth-emitted spectra, especially given the significant spectral variation of Earth targets. That is, although the OOB contribution of VIIRS is well known, it is not a uniform quantity applicable across all scene types. As such, this article quantifies OOB contribution for multiple relative spectral response characterization versions across the S-NPP, NOAA-20, and JPSS-2 VIIRS sensors as a function of varied SCIAMACHY- and IASI-measured hyperspectral Earth-reflected and Earth-emitted scenes. For instance, this paper reveals measured radiance variations of nearly 2% for the S-NPP VIIRS M5 (~0.67 μm) band, and up to 5.7% for certain VIIRS M9 (~1.38 μm) and M13 (~4.06 μm) bands that are owed solely to the truncation of OOB response for a set of spectrally distinct Earth scenes. If unmitigated, e.g., by only considering the published extended bandpass, such variations may directly translate to scene-dependent scaling discrepancies or subtle errors in vegetative index determinations. Therefore, knowledge of OOB effects is especially important for inter-calibration or environmental retrieval efforts that rely on specific or multiple categories of Earth scene spectra, and also to researchers whose products rely on the impacted channels. Additionally, instrument teams may find this evaluation method useful for pre-launch characterization of OOB contribution with specific Earth targets in mind rather than relying on general models. Full article

(This article belongs to the Special Issue Satellite Data Application, Validation and Calibration for Atmospheric Observation)

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29 pages, 10996 KiB

Open AccessFeature PaperArticle

Validation of Carbon Trace Gas Profile Retrievals from the NOAA-Unique Combined Atmospheric Processing System for the Cross-Track Infrared Sounder

by Nicholas R. Nalli, Changyi Tan, Juying Warner, Murty Divakarla, Antonia Gambacorta, Michael Wilson, Tong Zhu, Tianyuan Wang, Zigang Wei, Ken Pryor, Satya Kalluri, Lihang Zhou, Colm Sweeney, Bianca C. Baier, Kathryn McKain, Debra Wunch, Nicholas M. Deutscher, Frank Hase, Laura T. Iraci, Rigel Kivi, Isamu Morino, Justus Notholt, Hirofumi Ohyama, David F. Pollard, Yao Té, Voltaire A. Velazco, Thorsten Warneke, Ralf Sussmann and Markus Rettinger Show full author list Hide full author list

Remote Sens. 2020, 12(19), 3245; https://doi.org/10.3390/rs12193245 - 6 Oct 2020

Cited by 25 | Viewed by 5432

Abstract

This paper provides an overview of the validation of National Oceanic and Atmospheric Administration (NOAA) operational retrievals of atmospheric carbon trace gas profiles, specifically carbon monoxide (CO), methane (CH₄) and carbon dioxide (CO₂), from the NOAA-Unique Combined Atmospheric Processing [...] Read more.

This paper provides an overview of the validation of National Oceanic and Atmospheric Administration (NOAA) operational retrievals of atmospheric carbon trace gas profiles, specifically carbon monoxide (CO), methane (CH₄) and carbon dioxide (CO₂), from the NOAA-Unique Combined Atmospheric Processing System (NUCAPS), a NOAA enterprise algorithm that retrieves atmospheric profile environmental data records (EDRs) under global non-precipitating (clear to partly cloudy) conditions. Vertical information about atmospheric trace gases is obtained from the Cross-track Infrared Sounder (CrIS), an infrared Fourier transform spectrometer that measures high resolution Earth radiance spectra from NOAA operational low earth orbit (LEO) satellites, including the Suomi National Polar-orbiting Partnership (SNPP) and follow-on Joint Polar Satellite System (JPSS) series beginning with NOAA-20. The NUCAPS CO, CH₄, and CO₂ profile EDRs are rigorously validated in this paper using well-established independent truth datasets, namely total column data from ground-based Total Carbon Column Observing Network (TCCON) sites, and in situ vertical profile data obtained from aircraft and balloon platforms via the NASA Atmospheric Tomography (ATom) mission and NOAA AirCore sampler, respectively. Statistical analyses using these datasets demonstrate that the NUCAPS carbon gas profile EDRs generally meet JPSS Level 1 global performance requirements, with the absolute accuracy and precision of CO 5% and 15%, respectively, in layers where CrIS has vertical sensitivity; CH₄ and CO₂ product accuracies are both found to be within ±1%, with precisions of ≈1.5% and ⪅0.5%, respectively, throughout the tropospheric column. Full article

(This article belongs to the Special Issue Satellite Data Application, Validation and Calibration for Atmospheric Observation)

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29 pages, 7705 KiB

Open AccessArticle

Bias Correction of the Ratio of Total Column CH₄ to CO₂ Retrieved from GOSAT Spectra

by Haruki Oshio, Yukio Yoshida, Tsuneo Matsunaga, Nicholas M. Deutscher, Manvendra Dubey, David W. T. Griffith, Frank Hase, Laura T. Iraci, Rigel Kivi, Cheng Liu, Isamu Morino, Justus Notholt, Young-Suk Oh, Hirofumi Ohyama, Christof Petri, David F. Pollard, Coleen Roehl, Kei Shiomi, Ralf Sussmann, Yao Té, Voltaire A. Velazco, Thorsten Warneke and Debra Wunch Show full author list Hide full author list

Remote Sens. 2020, 12(19), 3155; https://doi.org/10.3390/rs12193155 - 25 Sep 2020

Cited by 2 | Viewed by 4807

Abstract

The proxy method, using the ratio of total column CH₄ to CO₂ to reduce the effects of common biases, has been used to retrieve column-averaged dry-air mole fraction of CH₄ from satellite data. The present study characterizes the remaining scattering [...] Read more.

The proxy method, using the ratio of total column CH₄ to CO₂ to reduce the effects of common biases, has been used to retrieve column-averaged dry-air mole fraction of CH₄ from satellite data. The present study characterizes the remaining scattering effects in the CH₄/CO₂ ratio component of the Greenhouse gases Observing SATellite (GOSAT) retrieval and uses them for bias correction. The variation of bias between the GOSAT and Total Carbon Column Observing Network (TCCON) ratio component with GOSAT data-derived variables was investigated. Then, it was revealed that the variability of the bias could be reduced by using four variables for the bias correction—namely, airmass, 2 μm band radiance normalized with its noise level, the ratio between the partial column-averaged dry-air mole fraction of CH₄ for the lower atmosphere and that for the upper atmosphere, and the difference in surface albedo between the CH₄ and CO₂ bands. The ratio of partial column CH₄ reduced the dependence of bias on the cloud fraction and the difference between hemispheres. In addition to the reduction of bias (from 0.43% to 0%), the precision (standard deviation of the difference between GOSAT and TCCON) was reduced from 0.61% to 0.55% by the correction. The bias and its temporal variation were reduced for each site: the mean and standard deviation of the mean bias for individual seasons were within 0.2% for most of the sites. Full article

(This article belongs to the Special Issue Satellite Data Application, Validation and Calibration for Atmospheric Observation)

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21 pages, 4048 KiB

Open AccessArticle

Inter-Calibration of AMSU-A Window Channels

by Wenze Yang, Huan Meng, Ralph R. Ferraro and Yong Chen

Remote Sens. 2020, 12(18), 2988; https://doi.org/10.3390/rs12182988 - 14 Sep 2020

Cited by 1 | Viewed by 2935

Abstract

More than one decade of observations from the Advanced Microwave Sounding Unit-A (AMSU-A) onboard the polar-orbiting satellites NOAA-15 to NOAA-19 and European Meteorological Operational satellite program-A (MetOp-A) provided global information on atmospheric temperature profiles, water vapor, cloud, precipitation, etc. These observations were primarily [...] Read more.

More than one decade of observations from the Advanced Microwave Sounding Unit-A (AMSU-A) onboard the polar-orbiting satellites NOAA-15 to NOAA-19 and European Meteorological Operational satellite program-A (MetOp-A) provided global information on atmospheric temperature profiles, water vapor, cloud, precipitation, etc. These observations were primarily intended for weather related prediction and applications, however, in order to meet the requirements for climate application, further reprocessing must be conducted to first eliminate any potential satellites biases. After the geolocation and cross-scan bias corrections were applied to the dataset, follow-on research focused on the comparison amongst AMSU-A window channels (e.g., 23.8, 31.4, 50.3 and 89.0 GHz) from the six different satellites to remove any inter-satellite inconsistency. Inter-satellite differences can arise from many error sources, such as bias drift, sun-heating-induced instrument variability in brightness temperatures, radiance dependent biases due to inaccurate calibration nonlinearity, etc. The Integrated microwave inter-calibration approach (IMICA) approach was adopted in this study for inter-satellite calibration of AMSU-A window channels after the appropriate standard deviation (STD) thresholds were identified to restrict Simultaneous Nadir Overpass (SNO) data for window channels. This was a critical step towards the development of a set of fundamental and thematic climate data records (CDRs) for hydrological and climatological applications. NOAA-15 served as the main reference satellite for this study. For ensuing studies that expand to beyond 2015, however, it is recommended that a different satellite be adopted as the reference due to concerns over potential degradation of NOAA-15 AMSU-A. Full article

(This article belongs to the Special Issue Satellite Data Application, Validation and Calibration for Atmospheric Observation)

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22 pages, 4263 KiB

Open AccessArticle

Calibration and Validation of Antenna and Brightness Temperatures from Metop-C Advanced Microwave Sounding Unit-A (AMSU-A)

by Banghua Yan, Junye Chen, Cheng-Zhi Zou, Khalil Ahmad, Haifeng Qian, Kevin Garrett, Tong Zhu, Dejiang Han and Joseph Green

Remote Sens. 2020, 12(18), 2978; https://doi.org/10.3390/rs12182978 - 14 Sep 2020

Cited by 8 | Viewed by 3159

Abstract

This study carries out the calibration and validation of Antenna Temperature Data Record (TDR) and Brightness Temperature Sensor Data Record (SDR) data from the last National Oceanic and Atmospheric Administration (NOAA) Advanced Microwave Sounding Unit-A (AMSU-A) flown on the Meteorological Operational satellite programme [...] Read more.

This study carries out the calibration and validation of Antenna Temperature Data Record (TDR) and Brightness Temperature Sensor Data Record (SDR) data from the last National Oceanic and Atmospheric Administration (NOAA) Advanced Microwave Sounding Unit-A (AMSU-A) flown on the Meteorological Operational satellite programme (MetOp)-C satellite. The calibration comprises the selection of optimal space view positions for the instrument and the determination of coefficients in calibration equations from the Raw Data Record (RDR) to TDR and SDR. The validation covers the analyses of the instrument noise equivalent differential temperature (NEDT) performance and the TDR and SDR data quality from the launch until 15 November 2019. In particular, the Metop-C data quality is assessed by comparing to radiative transfer model simulations and observations from Metop-A/B AMSU-A, respectively. The results demonstrate that the on-orbit instrument NEDTs have been stable since launch and continue to meet the specifications at most channels except for channel 3, whose NEDT exceeds the specification after April 2019. The quality of the Metop-C AMSU-A data for all channels except channel 3 have been reliable since launch. The quality at channel 3 is degraded due to the noise exceeding the specification. Compared to its TDR data, the Metop-C AMSU-A SDR data exhibit a reduced and more symmetric scan angle-dependent bias against radiative transfer model simulations, demonstrating the great performance of the TDR to SDR conversion coefficients. Additionally, the Metop-C AMSU-A data quality agrees well with Metop-A/B AMSU-A data, with an averaged difference in the order of 0.3 K, which is confirmed based on Simultaneous Nadir Overpass (SNO) inter-sensor comparisons between Metop-A/B/C AMSU-A instruments via either NOAA-18 or NOAA-19 AMSU-A as a transfer. Full article

(This article belongs to the Special Issue Satellite Data Application, Validation and Calibration for Atmospheric Observation)

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31 pages, 5571 KiB

Open AccessArticle

The Reprocessed Suomi NPP Satellite Observations

by Cheng-Zhi Zou, Lihang Zhou, Lin Lin, Ninghai Sun, Yong Chen, Lawrence E. Flynn, Bin Zhang, Changyong Cao, Flavio Iturbide-Sanchez, Trevor Beck, Banghua Yan, Satya Kalluri, Yan Bai, Slawomir Blonski, Taeyoung Choi, Murty Divakarla, Yalong Gu, Xianjun Hao, Wei Li, Ding Liang, Jianguo Niu, Xi Shao, Larrabee Strow, David C. Tobin, Denis Tremblay, Sirish Uprety, Wenhui Wang, Hui Xu, Hu Yang and Mitchell D. Goldberg Show full author list Hide full author list

Remote Sens. 2020, 12(18), 2891; https://doi.org/10.3390/rs12182891 - 6 Sep 2020

Cited by 25 | Viewed by 6064

Abstract

The launch of the National Oceanic and Atmospheric Administration (NOAA)/ National Aeronautics and Space Administration (NASA) Suomi National Polar-orbiting Partnership (S-NPP) and its follow-on NOAA Joint Polar Satellite Systems (JPSS) satellites marks the beginning of a new era of operational satellite observations of [...] Read more.

The launch of the National Oceanic and Atmospheric Administration (NOAA)/ National Aeronautics and Space Administration (NASA) Suomi National Polar-orbiting Partnership (S-NPP) and its follow-on NOAA Joint Polar Satellite Systems (JPSS) satellites marks the beginning of a new era of operational satellite observations of the Earth and atmosphere for environmental applications with high spatial resolution and sampling rate. The S-NPP and JPSS are equipped with five instruments, each with advanced design in Earth sampling, including the Advanced Technology Microwave Sounder (ATMS), the Cross-track Infrared Sounder (CrIS), the Ozone Mapping and Profiler Suite (OMPS), the Visible Infrared Imaging Radiometer Suite (VIIRS), and the Clouds and the Earth’s Radiant Energy System (CERES). Among them, the ATMS is the new generation of microwave sounder measuring temperature profiles from the surface to the upper stratosphere and moisture profiles from the surface to the upper troposphere, while CrIS is the first of a series of advanced operational hyperspectral sounders providing more accurate atmospheric and moisture sounding observations with higher vertical resolution for weather and climate applications. The OMPS instrument measures solar backscattered ultraviolet to provide information on the concentrations of ozone in the Earth’s atmosphere, and VIIRS provides global observations of a variety of essential environmental variables over the land, atmosphere, cryosphere, and ocean with visible and infrared imagery. The CERES instrument measures the solar energy reflected by the Earth, the longwave radiative emission from the Earth, and the role of cloud processes in the Earth’s energy balance. Presently, observations from several instruments on S-NPP and JPSS-1 (re-named NOAA-20 after launch) provide near real-time monitoring of the environmental changes and improve weather forecasting by assimilation into numerical weather prediction models. Envisioning the need for consistencies in satellite retrievals, improving climate reanalyses, development of climate data records, and improving numerical weather forecasting, the NOAA/Center for Satellite Applications and Research (STAR) has been reprocessing the S-NPP observations for ATMS, CrIS, OMPS, and VIIRS through their life cycle. This article provides a summary of the instrument observing principles, data characteristics, reprocessing approaches, calibration algorithms, and validation results of the reprocessed sensor data records. The reprocessing generated consistent Level-1 sensor data records using unified and consistent calibration algorithms for each instrument that removed artificial jumps in data owing to operational changes, instrument anomalies, contaminations by anomaly views of the environment or spacecraft, and other causes. The reprocessed sensor data records were compared with and validated against other observations for a consistency check whenever such data were available. The reprocessed data will be archived in the NOAA data center with the same format as the operational data and technical support for data requests. Such a reprocessing is expected to improve the efficiency of the use of the S-NPP and JPSS satellite data and the accuracy of the observed essential environmental variables through either consistent satellite retrievals or use of the reprocessed data in numerical data assimilations. Full article

(This article belongs to the Special Issue Satellite Data Application, Validation and Calibration for Atmospheric Observation)

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18 pages, 2879 KiB

Open AccessArticle

Spatio-Temporal Variability of Aerosol Optical Depth, Total Ozone and NO₂ Over East Asia: Strategy for the Validation to the GEMS Scientific Products

by Sang Seo Park, Sang-Woo Kim, Chang-Keun Song, Jong-Uk Park and Kang-Ho Bae

Remote Sens. 2020, 12(14), 2256; https://doi.org/10.3390/rs12142256 - 14 Jul 2020

Cited by 14 | Viewed by 3993

Abstract

In this study, the spatio-temporal variability of aerosol optical depth (AOD), total column ozone (TCO), and total column NO₂ (TCN) was identified over East Asia using long-term datasets from ground-based and satellite observations. Based on the statistical results, optimized spatio-temporal ranges for [...] Read more.

In this study, the spatio-temporal variability of aerosol optical depth (AOD), total column ozone (TCO), and total column NO₂ (TCN) was identified over East Asia using long-term datasets from ground-based and satellite observations. Based on the statistical results, optimized spatio-temporal ranges for the validation study were determined with respect to the target materials. To determine both spatial and temporal ranges for the validation study, we confirmed that the observed datasets can be statistically considered as the same quantity within the ranges. Based on the thresholds of R²>0.95 (temporal) and R>0.95 (spatial), the basic ranges for spatial and temporal scales for AOD validation was within 30 km and 30 min, respectively. Furthermore, the spatial scales for AOD validation showed seasonal variation, which expanded the range to 40 km in summer and autumn. Because of the seasonal change of latitudinal gradient of the TCO, the seasonal variation of the north-south range is a considerable point. For the TCO validation, the north-south range is varied from 0.87° in spring to 1.05° in summer. The spatio-temporal range for TCN validation was 20 min (temporal) and 20–50 km (spatial). However, the nearest value of satellite data was used in the validation because the spatio-temporal variation of TCN is large in summer and autumn. Estimation of the spatio-temporal variability for respective pollutants may contribute to improving the validation of satellite products. Full article

(This article belongs to the Special Issue Satellite Data Application, Validation and Calibration for Atmospheric Observation)

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19 pages, 6132 KiB

Open AccessArticle

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

by Bingkun Luo and Peter J. Minnett

Remote Sens. 2020, 12(11), 1873; https://doi.org/10.3390/rs12111873 - 9 Jun 2020

Cited by 23 | Viewed by 5552

Abstract

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 (SST_skin) with infrared radiometers onboard earth observation satellites and shipboard instruments is a mature subject spanning [...] Read more.

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 (SST_skin) with infrared radiometers onboard earth observation satellites and shipboard instruments is a mature subject spanning several decades. Reanalysis model output SST_skin, 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 SST_skin data must be rigorously evaluated to meet the stringent accuracy requirements for climate research. This study aims to estimate the accuracy of the ERA5 SST_skin 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 SST_skin 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 SST_skin differences for ERA5. The SST_skin differences are greater with the large, positive air–sea temperature differences. This study provides suggestions for the applicability of ERA5 SST_skin fields in a selection of research applications. Full article

(This article belongs to the Special Issue Satellite Data Application, Validation and Calibration for Atmospheric Observation)

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20 pages, 390 KiB

Open AccessArticle

Characterization of the High-Resolution Infrared Radiation Sounder Using Lunar Observations

by Martin J. Burgdorf, Thomas G. Müller, Stefan A. Buehler, Marc Prange and Manfred Brath

Remote Sens. 2020, 12(9), 1488; https://doi.org/10.3390/rs12091488 - 7 May 2020

Cited by 3 | Viewed by 2908

Abstract

The High-Resolution Infrared Radiation Sounder (HIRS) has been operational since 1975 on different satellites. In spite of this long utilization period, the available information about some of its basic properties is incomplete or contradictory. We have approached this problem by analyzing intrusions of [...] Read more.

The High-Resolution Infrared Radiation Sounder (HIRS) has been operational since 1975 on different satellites. In spite of this long utilization period, the available information about some of its basic properties is incomplete or contradictory. We have approached this problem by analyzing intrusions of the Moon in the deep space view of HIRS/2 through HIRS/4. With this method we found: (1) The diameters of the field of view of HIRS/2, HIRS/3, and HIRS/4 have the relative proportions of

1.4 °

to

1.3 °

to

0.7 °

with all channels; (2) the co-registration differs by up to

0.031 °

among the long-wave and by up to

0.015 °

among the shortwave spectral channels in the along-track direction; (3) the photometric calibration is consistent within 0.7% or less for channels 2–7 (1.2% for HIRS/2), similar values were found for channels 13–16; (4) the non-linearity of the short-wavelength channels is negligible; and (5) the contribution of reflected sunlight to the flux in the short-wavelength channels can be determined in good approximation, if the emissivity of the surface is known. Full article

(This article belongs to the Special Issue Satellite Data Application, Validation and Calibration for Atmospheric Observation)

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19 pages, 6625 KiB

Open AccessArticle

Surface Diffuse Solar Radiation Determined by Reanalysis and Satellite over East Asia: Evaluation and Comparison

by Hou Jiang, Yaping Yang, Hongzhi Wang, Yongqing Bai and Yan Bai

Remote Sens. 2020, 12(9), 1387; https://doi.org/10.3390/rs12091387 - 28 Apr 2020

Cited by 35 | Viewed by 3997

Abstract

Recently, surface diffuse solar radiation (R_dif) has been attracting a growing interest in view of its function in improving plant productivity, thus promoting global carbon uptake, and its impacts on solar energy utilization. To date, very few radiation products provide estimates [...] Read more.

Recently, surface diffuse solar radiation (R_dif) has been attracting a growing interest in view of its function in improving plant productivity, thus promoting global carbon uptake, and its impacts on solar energy utilization. To date, very few radiation products provide estimates of R_dif, and systematic validation and evaluation are even more scare. In this study, R_dif estimates from Reanalysis Fifth Generation (ERA5) of European Center for Medium-Range Weather Forecasts and satellite-based retrieval (called JiEA) are evaluated over East Asia using ground measurements at 39 stations from World Radiation Data Center (WRDC) and China Meteorological Administration (CMA). The results show that JiEA agrees well with measurements, while ERA5 underestimates R_dif significantly. Both datasets perform better at monthly mean scale than at daily mean and hourly scale. The mean bias error and root-mean-square error of daily mean estimates are −1.21 W/m² and 20.06 W/m² for JiEA and −17.18 W/m² and 32.42 W/m² for ERA5, respectively. Regardless of over- or underestimation, correlations of estimated time series of ERA5 and JiEA show high similarity. JiEA reveals a slight decreasing trend at regional scale, but ERA5 shows no significant trend, and neither of them reproduces temporal variability of ground measurements. Data accuracy of ERA5 is more robust than JiEA in time but less in space. Latitudinal dependency is noted for ERA5 while not for JiEA. In addition, spatial distributions of R_dif from ERA5 and JiEA show pronounced discrepancy. Neglect of adjacency effects caused by horizontal photon transport is the main cause for R_dif underestimation of ERA5. Spatial analysis calls for improvements to the representation of clouds, aerosols and water vapor for reproducing fine spatial distribution and seasonal variations of R_dif. Full article

(This article belongs to the Special Issue Satellite Data Application, Validation and Calibration for Atmospheric Observation)

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24 pages, 6967 KiB

Open AccessArticle

NOAA Operational Microwave Sounding Radiometer Data Quality Monitoring and Anomaly Assessment Using COSMIC GNSS Radio-Occultation Soundings

by Robbie Iacovazzi, Lin Lin, Ninghai Sun and Quanhua Liu

Remote Sens. 2020, 12(5), 828; https://doi.org/10.3390/rs12050828 - 4 Mar 2020

Cited by 13 | Viewed by 3975

Abstract

National Oceanic and Atmospheric Administration (NOAA) operational Advanced Technology Microwave Sounder (ATMS) and Advanced Microwave Sounding Unit-A (AMSU-A) data used in numerical weather prediction and climate analysis are essential to protect life and property and maintain safe and efficient commerce. Routine data quality [...] Read more.

National Oceanic and Atmospheric Administration (NOAA) operational Advanced Technology Microwave Sounder (ATMS) and Advanced Microwave Sounding Unit-A (AMSU-A) data used in numerical weather prediction and climate analysis are essential to protect life and property and maintain safe and efficient commerce. Routine data quality monitoring and anomaly assessment is important to sustain data effectiveness. One valuable parameter used to monitor microwave sounder data quality is the antenna temperature (Ta) difference (O-B) computed between direct instrument Ta measurements and forward radiative transfer model (RTM) brightness temperature (Tb) simulations. This requires microwave radiometer data to be collocated with atmospheric temperature and moisture sounding profiles, so that representative boundary conditions are used to produce the RTM-simulated Tb values. In this study, Constellation Observing System for Meteorology, Ionosphere, and Climate/Formosa Satellite Mission 3 (COSMIC) Global Navigation Satellite System (GNSS) Radio Occultation (RO) soundings over the ocean and equatorward of 60° latitude are used as input to the Community RTM (CRTM) to generate simulated NOAA-18, NOAA-19, Metop-A, and Metop-B AMSU-A and S-NPP and NOAA-20 ATMS Tb values. These simulated Tb values, together with observed Ta values that are nearly simultaneous in space and time, are used to compute Ta O-B statistics on monthly time scales for each instrument. In addition, the CRTM-simulated Tb values based on the COSMIC GNSS RO soundings can be used as a transfer standard to inter-compare Ta values from different microwave radiometer makes and models that have the same bands. For example, monthly Ta O-B statistics for NOAA-18 AMSU-A Channels 4–12 and NOAA-20 ATMS Channels 5–13 can be differenced to estimate the “double-difference” Ta biases between these two instruments for the corresponding frequency bands. This study reveals that the GNSS RO soundings are critical to monitoring and trending individual instrument O-B Ta biases and inter-instrument “double-difference” Ta biases and also to estimate impacts of some sensor anomalies on instrument Ta values. Full article

(This article belongs to the Special Issue Satellite Data Application, Validation and Calibration for Atmospheric Observation)

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21 pages, 20716 KiB

Open AccessArticle

The New Potential of Deep Convective Clouds as a Calibration Target for a Geostationary UV/VIS Hyperspectral Spectrometer

by Yeeun Lee, Myoung-Hwan Ahn and Mina Kang

Remote Sens. 2020, 12(3), 446; https://doi.org/10.3390/rs12030446 - 1 Feb 2020

Cited by 7 | Viewed by 3508

Abstract

As one of geostationary earth orbit constellation for environmental monitoring over the next decade, the Geostationary Environment Monitoring Spectrometer (GEMS) has been designed to observe the Asia-Pacific region to provide information on atmospheric chemicals, aerosols, and cloud properties. In order to continuously monitor [...] Read more.

As one of geostationary earth orbit constellation for environmental monitoring over the next decade, the Geostationary Environment Monitoring Spectrometer (GEMS) has been designed to observe the Asia-Pacific region to provide information on atmospheric chemicals, aerosols, and cloud properties. In order to continuously monitor sensor performance after its launch in early 2020, we suggest in this paper deep convective clouds (DCCs) as a possible target for the vicarious calibration of the GEMS, the first ultraviolet and visible hyperspectral sensor onboard a geostationary satellite. The Tropospheric Monitoring Instrument (TROPOMI) and the Ozone Monitoring Instrument (OMI) are used as a proxy for GEMS, and a conventional DCC-detection approach applying a thermal threshold test is used for DCC detection based on collocations with the Advanced Himawari Imager (AHI) onboard the Himawari-8 geostationary satellite. DCCs are frequently detected over the GEMS observation area at an average of over 200 pixels within a single observation scene. Considering the spatial resolution of the GEMS (3.5

\times

8 km²), which is similar to the TROPOMI and its temporal resolution (eight times a day), the availability of DCCs is expected to be sufficient for the vicarious calibration of the GEMS. Inspection of the DCC reflectivity spectra estimated from OMI and TROPOMI data also shows promising results. The estimated DCC spectra are in good agreement within a known uncertainty range with comparable spectral features even with different observation geometries and sensor characteristics. When DCC detection is improved further by applying both visible and infrared tests, the variability of DCC reflectivity from TROPOMI data is reduced from 10% to 5%. This is mainly due to the efficient screening out of cold, thin cirrus clouds in the visible test and of bright, warm clouds in the infrared test. Precise DCC detection is also expected to contribute to the accurate characterization of cloud reflectivity, which will be investigated further in future research. Full article

(This article belongs to the Special Issue Satellite Data Application, Validation and Calibration for Atmospheric Observation)

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17 pages, 2391 KiB

Open AccessLetter

Using the BFAST Algorithm and Multitemporal AIRS Data to Investigate Variation of Atmospheric Methane Concentration over Zoige Wetland of China

by Yuanyuan Yang and Yong Wang

Remote Sens. 2020, 12(19), 3199; https://doi.org/10.3390/rs12193199 - 30 Sep 2020

Cited by 6 | Viewed by 3175

Abstract

The monitoring of wetland methane (CH₄) emission is essential in the context of global CH₄ emission and climate change. The remotely sensed multitemporal Atmospheric Infrared Sounder (AIRS) CH₄ data and the Breaks for Additive Season and Trend (BFAST) algorithm [...] Read more.

The monitoring of wetland methane (CH₄) emission is essential in the context of global CH₄ emission and climate change. The remotely sensed multitemporal Atmospheric Infrared Sounder (AIRS) CH₄ data and the Breaks for Additive Season and Trend (BFAST) algorithm were used to detect atmospheric CH₄ dynamics in the Zoige wetland, China between 2002 and 2018. The overall atmospheric CH₄ concentration increased steadily with a rate of 5.7 ± 0.3 ppb/year. After decomposing the time-series of CH₄ data using the BFAST algorithm, we found no anomalies in the seasonal and error components. The trend component increased with time, and a total of seven breaks were detected within four cells. Six were well-explained by the air temperature anomalies primarily, but one break was not. The effect of parameter h on decomposition outcomes was studied because it could influence the number of breaks in the trend component. As h increased, the number of breaks decreased. The interplays of the observations of interest, break numbers, and statistical significance should determine the h value. Full article

(This article belongs to the Special Issue Satellite Data Application, Validation and Calibration for Atmospheric Observation)

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15 pages, 5170 KiB

Open AccessTechnical Note

Development of a Machine Learning-Based Radiometric Bias Correction for NOAA’s Microwave Integrated Retrieval System (MiRS)

by Yan Zhou and Christopher Grassotti

Remote Sens. 2020, 12(19), 3160; https://doi.org/10.3390/rs12193160 - 26 Sep 2020

Cited by 15 | Viewed by 4392

Abstract

We present the development of a dynamic over-ocean radiometric bias correction for the Microwave Integrated Retrieval System (MiRS) which accounts for spatial, temporal, spectral, and angular dependence of the systematic differences between observed and forward model-simulated radiances. The dynamic bias correction, which utilizes [...] Read more.

We present the development of a dynamic over-ocean radiometric bias correction for the Microwave Integrated Retrieval System (MiRS) which accounts for spatial, temporal, spectral, and angular dependence of the systematic differences between observed and forward model-simulated radiances. The dynamic bias correction, which utilizes a deep neural network approach, is designed to incorporate dependence on the atmospheric and surface conditions that impact forward model biases. The approach utilizes collocations of observed Suomi National Polar-orbiting Partnership/Advanced Technology Microwave Sounder (SNPP/ATMS) radiances and European Centre for Medium-Range Weather Forecasts (ECMWF) model analyses which are used as input to the Community Radiative Transfer Model (CRTM) forward model to develop training data of radiometric biases. Analysis of the neural network performance indicates that in many channels, the dynamic bias is able to reproduce realistically both the spatial patterns of the original bias and its probability distribution function. Furthermore, retrieval impact experiments on independent data show that, compared with the baseline static bias correction, using the dynamic bias correction can improve temperature and water vapor profile retrievals, particularly in regions with higher Cloud Liquid Water (CLW) amounts. Ocean surface emissivity retrievals are also improved, for example at 23.8 GHz, showing an increase in correlation from 0.59 to 0.67 and a reduction of standard deviation from 0.035 to 0.026. Full article

(This article belongs to the Special Issue Satellite Data Application, Validation and Calibration for Atmospheric Observation)

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10 pages, 3072 KiB

Open AccessLetter

Evaluating the Absolute Calibration Accuracy and Stability of AIRS Using the CMC SST

by Hartmut H. Aumann, Steven E. Broberg, Evan M. Manning, Thomas S. Pagano and Robert C. Wilson

Remote Sens. 2020, 12(17), 2743; https://doi.org/10.3390/rs12172743 - 25 Aug 2020

Cited by 2 | Viewed by 2388

Abstract

We compare the daily mean and standard deviation of the difference between the sea surface skin temperature (SST) derived from clear sky Atmospheric InfraRed Sounder (AIRS) data from seven atmospheric window channels between 2002 and 2020 and collocated Canadian Meteorological Centre (CMC) SST [...] Read more.

We compare the daily mean and standard deviation of the difference between the sea surface skin temperature (SST) derived from clear sky Atmospheric InfraRed Sounder (AIRS) data from seven atmospheric window channels between 2002 and 2020 and collocated Canadian Meteorological Centre (CMC) SST data from the tropical oceans. After correcting the mean difference for cloud contamination and diurnal effects, the remaining bias relative to the CMC SST, is reasonably consistent with estimates of the AIRS absolute accuracy based on the uncertainty of the pre-launch calibration. The time series of the bias produces trends well below the 10 mK/yr level required for climate change evaluations. The trends are in the 2 mK/yr range for the five window channels between 790 and 1231 cm⁻¹, and +5 mK/yr for the shortwave channels. Between 2002 and 2020, the time series of the standard deviation of the difference between the AIRS SST and the CMC SST dropped fairly steadily to below 0.4 K in several AIRS window channels, a level previously only seen in gridded SST products relative to the Argo buoys. Full article

(This article belongs to the Special Issue Satellite Data Application, Validation and Calibration for Atmospheric Observation)

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14 pages, 3273 KiB

Open AccessLetter

Evaluation of the Diurnal Variation of Upper Tropospheric Humidity in Reanalysis Using Homogenized Observed Radiances from International Geostationary Weather Satellites

by Yunheng Xue, Jun Li, Zhenglong Li, Mathew M. Gunshor and Timothy J. Schmit

Remote Sens. 2020, 12(10), 1628; https://doi.org/10.3390/rs12101628 - 19 May 2020

Cited by 11 | Viewed by 3096

Abstract

A near global dataset of homogenized clear-sky 6.5-μm brightness temperatures (BTs) from international geostationary (GEO) weather satellites has recently been generated and validated. In this study, these radiance measurements are used to construct the diurnal variation of upper tropospheric humidity (UTH) and to [...] Read more.

A near global dataset of homogenized clear-sky 6.5-μm brightness temperatures (BTs) from international geostationary (GEO) weather satellites has recently been generated and validated. In this study, these radiance measurements are used to construct the diurnal variation of upper tropospheric humidity (UTH) and to evaluate these diurnal variations simulated by five reanalysis datasets over the 45° N–45° S region. The features of the diurnal variation described by the new dataset are comparable with previous observational studies that a land–sea contrast in the diurnal variation of UTH is exhibited. Distinct diurnal variations are observed over the deep convective regions where high UTH exists. The evaluation of reanalysis datasets indicates that reanalysis systems still have considerable difficulties in capturing the observed features of the diurnal variation of UTH. All five reanalysis datasets present the largest wet biases in the afternoon when the observed UTH experiences a diurnal minimum. Reanalysis can roughly reproduce the day–night contrast of UTH but with much weaker amplitudes and later peak time over both land and ocean. Comparison of the geographical distribution of the diurnal variation shows that both ERA5 and MERRA-2 could capture the larger diurnal variations over convective regions. However, the diurnal amplitudes are widely underestimated, especially over convective land regions, while the phase biases are relatively larger over open oceans. These results suggest that some deficiencies may exist in convection and cloud parameterization schemes in reanalysis models. Full article

(This article belongs to the Special Issue Satellite Data Application, Validation and Calibration for Atmospheric Observation)

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