Brightness Temperature Biases Research Articles

Quantifying the Impact of Aerosols on Geostationary Satellite Infrared Radiance Simulations: A Study with Himawari-8 AHI

Aerosols exert a significant influence on the brightness temperature observed in the thermal infrared (IR) channels, yet the specific contributions of various aerosol types remain underexplored. This study integrated the Copernicus Atmosphere Monitoring Service (CAMS) atmospheric composition reanalysis data into the Radiative Transfer for TOVS (RTTOV) model to quantify the aerosol effects on brightness temperature (BT) simulations for the Advanced Himawari Imager (AHI) aboard the Himawari-8 geostationary satellite. Two distinct experiments were conducted: the aerosol-aware experiment (AER), which accounted for aerosol radiative effects, and the control experiment (CTL), in which aerosol radiative effects were omitted. The CTL experiment results reveal uniform negative bias (observation minus background (O-B)) across all six IR channels of the AHI, with a maximum deviation of approximately −1 K. Conversely, the AER experiment showed a pronounced reduction in innovation, which was especially notable in the 10.4 μm channel, where the bias decreased by 0.7 K. The study evaluated the radiative effects of eleven aerosol species, all of which demonstrated cooling effects in the AHI’s six IR channels, with dust aerosols contributing the most significantly (approximately 86%). In scenarios dominated by dust, incorporating the radiative effect of dust aerosols could correct the brightness temperature bias by up to 2 K, underscoring the substantial enhancement in the BT simulation for the 10.4 μm channel during dust events. Jacobians were calculated to further examine the RTTOV simulations’ sensitivity to aerosol presence. A clear temporal and spatial correlation between the dust concentration and BT simulation bias corroborated the critical role of the infrared channel data assimilation on geostationary satellites in capturing small-scale, rapidly developing pollution processes.

Characterization and Validation of ECOSTRESS Sea Surface Temperature Measurements at 70 m Spatial Scale

The ECOSTRESS push-whisk thermal radiometer on the International Space Station provides the highest spatial resolution temperature retrievals over the ocean that are currently available. It is a precursor to the future TRISHNA (CNES/ISRO), SBG (NASA), and LSTM (ESA) 50 to 70 m scale missions. Radiance transfer simulations and triple collocations with in situ ocean observations and NOAA L2P geostationary satellite ocean temperature retrievals were used to characterize brightness temperature biases and their sources in ECOSTRESS Collection 1 (software Build 6) data for the period 12 January 2019 to 31 October 2022. Radiometric noise, non-uniformities in the focal plane array, and black body temperature dynamics were characterized in ocean scenes using L1A raw instrument data, L1B calibrated radiances, and L2 skin temperatures. The mean brightness temperature biases were −1.74, −1.45, and −1.77 K relative to radiance transfer simulations in the 8.78, 10.49, and 12.09 µm wavelength bands, respectively, and skin temperatures had a −1.07 K bias relative to in situ observations. Cross-track noise levels range from 60 to 600 mK and vary systematically along the focal plane array and as a function of wavelength band and scene temperature. Overall, radiometric uncertainty is most strongly influenced by cross-track noise levels and focal plane non-uniformity. Production of an ECOSTRESS sea surface temperature product that meets the requirements of the SST community will require calibration methods that reduce the biases, noise levels, and focal plane non-uniformities.

Absolute radiometric calibration evaluation of the thermal infrared spectrometer onboard SDGSAT-1

ABSTRACTA thermal infrared spectrometer is an important sensor onboard the SDGSAT-1 dedicated to serving the 2030 Agenda for Sustainable Development of the United Nations. Field campaigns were conducted from January to December 2022 to evaluate its radiometric calibrations. A radiance-based calibration method was used to derive the spectral radiance of the lake surface around the satellite overpass using a boat-deployed TIR spectrometer or radiometer. The radiometric calibration conditions were quantified by examining the temperature bias between the satellite-based brightness temperature and the predicted at-sensor brightness temperature. Our results indicated that the average brightness temperature bias reached 0.772 K, −1.032 K, and −0.300 K, with RMSEs of 1.00, 1.10, and 0.43 K in bands 1, 2 and 3, respectively. Eighty percent of the field experiments achieved a high calibration accuracy with temperature biases of less than 1 K, and the calibration accuracy of band 3 was better than that of other bands. Atmospheric conditions and surface characterization play important roles in determining the calibration accuracy through surface observations and atmospheric transfer simulations. With the increasing need for SDGSAT-1 products, periodic field experiments are needed to monitor and improve the operational radiometric calibration of satellite instruments to create high-resolution and reasonable scientific datasets.

On the use of Infrared Atmospheric Sounding Interferometer (IASI) spectrally resolved radiances to test the EC-Earth climate model (v3.3.3) in clear-sky conditions

Abstract. The long-term comparison between simulated and observed spectrally resolved outgoing longwave radiation (OLR) can represent a stringent test for the direct verification and improvement of general circulation models (GCMs), which are regularly tuned by adjusting parameters related to subgrid processes not explicitly represented in the model to constrain the integrated OLR energy fluxes to observed values. However, a good agreement between simulated and observed integrated OLR fluxes may be obtained from the cancellation of opposite-in-sign systematic errors localized in specific spectral ranges. Since the mid-2000s, stable hyperspectral observations of the mid-infrared region (667 to 2750 cm−1) of the Earth emission spectrum have been provided by different sensors (e.g. AIRS, IASI and CrIS). Furthermore, the FORUM (Far-infrared Outgoing Radiation Understanding and Monitoring) mission, selected to be the ninth ESA Earth Explorer, will measure, starting from 2027, the terrestrial radiation emitted to space at the top of the atmosphere (TOA) from 100 to 1600 cm−1, filling the observational gap in the far-infrared (FIR) region, from 100 to 667 cm−1. In this work, in anticipation of FORUM measurements, we compare Infrared Atmospheric Sounding Interferometer (IASI) Metop-A observations to radiances simulated on the basis of the atmospheric fields predicted by the EC-Earth Global Climate Model (version 3.3.3) in clear-sky conditions. To simulate spectra based on the atmospheric and surface state provided by the climate model, the radiative transfer model σ-IASI has been integrated in the Cloud Feedback Model Intercomparison Project (COSP) package. Therefore, online simulations, provided by the EC-Earth model equipped with the new COSP–σ-IASI module, have been performed in clear-sky conditions with prescribed sea surface temperature and sea ice concentration, every 6 h, over a time frame consistent with the availability of IASI data. Systematic comparisons between observed and simulated brightness temperature (BT) have been performed in 10 cm−1 spectral intervals, on a global scale over the ocean, with a specific focus on the latitudinal belt between 30∘ S and 30∘ N. The analysis has shown a warm BT bias of about 3.5 K in the core of the CO2 absorption band and a cold BT bias of approximately 1 K in the wing of the CO2 band, due to a positive temperature bias in the stratosphere and a negative temperature bias in the middle troposphere of the climate model, respectively. Finally, considering a warm BT bias in the rotational–vibrational water vapour band, we have highlighted a dry bias of the water vapour concentration in the upper troposphere of the model.

Radiance consistency and deviation characteristics in the regions of overlap between three new-generation GEO meteorological satellites

ABSTRACT The combination of multi-source Geostationary Earth Orbit (GEO) satellites can realize simultaneous global observation with high frequency, which is significant to fine weather forecast and global climate change research. Here the radiance consistency and deviation characterization in the overlapping regions are analysed for three new generation GEO satellites, including the Advanced Himawari Imager (AHI) onboard the Himawari-8 satellite, the Advanced Baseline Imager (ABI) onboard the Geostationary Operational Environmental Satellite-17 (GOES-17) and the Advanced Geostationary Radiation Imager (AGRI) onboard FengYun satellite-4A (FY-4A). The result shows that the radiance has higher consistency for visible and near-infrared (VNIR) channels with correlation coefficient r > 0.98 than infrared (IR) channels with r > 0.90 in the overlapping regions of different GEO satellites. For FY-4A & Himawari-8, the average percentage deviation (APD) of VNIR channels is 5.54%–9.03%. The brightness temperature (BT) bias of IR channels is 1.16 K-2.50 K. For GOES-17 & Himawari-8, the APD of VNIR channels is 4.48%–6.71%, and the BT bias of IR channels is 0.96 K −2.76 K. Solar-viewing geometries significantly influence the radiance deviation in the over regions. The future work is to design a radiance fusion method and achieve cloud detection in ample space with high frequency by combining multi-source GEO satellites.

An Indoor Microwave Radiometer for Measurement of Tropospheric Water

This paper presents the first detailed description of the innovative measurement set up of an indoor tropospheric microwave radiometer (TROWARA) that avoids water films on radome. We discuss the performance of a commercial outdoor microwave radiometer (HATPRO) for measuring tropospheric water parameters in Bern, Switzerland. The HATPRO is less than 20 meters from the TROWARA and has different instrument characteristics. Brightness temperatures measured by HATPRO are analyzed by comparing them with coincident measurements from TROWARA and radiative transfer simulations based on the ECMWF operational analysis data (denoted as RTSE). To find the source of brightness temperature bias, a gradient boosting decision tree is used to analyze the sensitivity of eight feature factors to bias. Data processing routines of the two radiometers use different algorithms to retrieve integrated water vapour (IWV) and integrated cloud liquid water (ILW), whereas the same physical algorithms based on the radiative transfer equation are applied to obtain the opacity and rain rate. Using 62 days of data with varied weather conditions, it was found that TROWARA brightness temperatures are in good agreement with RTSE. HATPRO brightness temperatures are significantly overestimated by about 5 K at 22 GHz, compared to TROWARA and RTSE. HATPRO brightness temperatures at 31 GHz agree well with TROWARA and RTSE (within about +/-1 K). The overestimated brightness temperatures in the K-band and the HATPRO retrieval algorithm lead to an overestimation of IWV and ILW by HATPRO. The opacities at 31 GHz match very well for TROWARA and HATPRO during no rain with a verified R2 of 0.96. However, liquid water floating or remaining water films on the radome of the outdoor HATPRO radiometer induce an overestimation of the rain rate. The physical reason for the overestimated 22 GHz brightness temperatures of the HATPRO is mainly the result of the combined effect of instrument calibration, the surrounding environment of the instrument, and the sun elevation angle. This can be a problem with the Generation 2 HATPRO radiometer and this problem was resolved in the Generation 5 HATPRO radiometer.

Investigation of the Convection‐Allowing Prediction Error of an Extreme Precipitation Event of China Using CRTM‐Simulated Brightness Temperature

AbstractAn extreme rainfall case occurred on 20 July 2016 in northern China, producing over 600 mm of maximum 24 hr accumulated rainfall. The case is characterized by low‐echo centroid (LEC) based on radar observations. It is simulated by Weather Research and Forecasting model at 4 km grid spacing using the Morrison two‐moment microphysics scheme. Infrared brightness temperature (BT) simulated using a radiative transfer model together with simulated reflectivity are used to evaluate the simulated cloud structures. In general, the model predicts the rainfall amount, location, and propagation well. However, it fails to accurately predict the three‐dimensional cloud structures. The predicted convective cores (>35 dBZ) are higher than the freezing level, suggesting the presence of active cold‐cloud processes while the observed LEC suggests that warm‐cloud processes dominate. The simulation also produces too many upper level clouds and over‐predicts the fractions of overshooting clouds, resulting great over‐prediction of cloud top height (CTH). Additionally, the sensitivity of simulated BT to predicted cloud properties including CTH, cloud species and hydrometeor effective radii are examined to better understand the sources of error in simulated BT. Among those factors, CTH is found to be most critical to BT simulation. For every kilometer of CTH over‐prediction, there is about 6.431 K of negative BT bias owing to the lapse rate and the absorption and scattering effects of cloud particles. For most cloudy regions, cloud ice dominates the effect on simulated BT. Using diagnosed effective radii of simulated hydrometeors within the radiative transfer model results in small improvement to the BT simulation.

Assessing FY-3E HIRAS-II Radiance Accuracy Using AHI and MERSI-LL

The FY-3E/HIRAS-II (Hyperspectral Infrared Atmospheric Sounder-II), as an infrared hyperspectral instrument onboard the world’s first early morning polar-orbiting satellite, plays a major role in improving the accuracy and timeliness of global numerical weather predictions. In order to assess its observation quality, the geometrically, temporally, and spatially matched scene homogeneous HIRAS-II hyperspectral observations were convolved to the channels corresponding to the Himawari-8/AHI (Advanced Himawari Imager) and FY-3E/MERSI-LL (Medium-Resolution Spectral Imager) imagers from 15 March to 21 April 2022, and their brightness temperature deviation characteristics were statistically calculated in this paper. The results show that the HIRAS-II in-orbit observed brightness temperatures are slightly warmer than the AHI observations in all the matched AHI channels (long wave infrared channel 8 to channel 16) with a mean brightness temperature bias less than 0.65 K. The bias of the atmospheric absorption channel is slightly larger than that of the window channel. A standard deviation less than 0.31 K and a correlation coefficient higher than 0.98 in all channels means that the quality of the observation is satisfactory. The thresholds chosen for the colocation approximation factors (e.g., observation geometry angle, scene uniformity, observation azimuth, and observation time) for matching the HIRAS-II with AHI contribute little and negligible uncertainty to the bias assessment, so the difference between the two observed radiations is considered to be mainly from the systematic bias of the two-instrument measurement. Compared with MERSI-LL window channel 5, the observations of both instruments are very close, with a mean bias of 0.002 K and a standard deviation of 0.31 K. The mean brightness temperature bias (HIRAS-II minus MERSI-LL) of the MERSI-LL water vapor channel 4 is 0.66 K with a standard deviation of 0.22 K. The mean brightness temperature bias of channel 6 and channel 7 is 0.63 K (the standard deviation is 0.36 K) and 0.5 K (the standard deviation is 0.3 K), respectively. The biases of channel 4 are significantly and positively correlated with the target scene temperature, and the biases of channel 6 and 7 show a U-shaped change with the increase in the scene temperature, and the biases are smallest (close to 0 K) when the scene temperature is between 250 K and 280 K. The statistical characteristics of the HIRAS-II–MERSI-LL difference vary minimally and almost constantly over a period of time, indicating that the performance of the HIRAS-II instrument is stable.

Characterization of Brightness Temperature Biases at Channels 13 and 14 for FY-3C MWHS-2

The second generation Microwave Humidity Sounder (MWHS-2) onboard Fengyun (FY)-3C has a data quality comparable to that of counterpart microwave moisture sounders. However, channels 13 and 14 have large biases that are negatively correlated with the instrument temperature and prevent the operational assimilation of the data of these two channels. To better understand the biases of channels 13 and 14, the correlation of the observation minus simulation data (O-B bias) of different channels with the instrument temperature and other factors is investigated for FY-3C MWHS-2 in this paper. A sensitivity analysis using a gradient boosting decision tree indicates that the instrument temperature and scan position are the two dominant factors, with total bias-contribution scores exceeding 0.8 for both channels 13 and 14. This conclusion is verified through further analysis of the bias distributions for the scan position, scene brightness temperature and ascending/descending orbits. Using 12-week data recorded in 2016, the correlations of the O-B bias with the scan position and instrument temperature are specifically investigated and a correction algorithm is formulated, with which data for 2016 and 2017 are corrected. The corrected data in both channels have smaller, more stable biases, and are less affected by the instrument temperatures and scan position. The bias contributions associated with these two factors should thus be further studied.

Consistency and Stability of SNPP ATMS Microwave Observations and COSMIC-2 Radio Occultation over Oceans

Radio occultation (RO) sensor measurements have critical roles in numerical weather prediction (NWP) by complementing microwave and infrared sounder measurements with information of the atmospheric profiles at high accuracy, precision, and vertical resolution. This study evaluates Constellation Observing System for Meteorology, Ionosphere, and Climate 2 (COSMIC-2) wet temperature and humidity data products’ consistency and stability through inter-comparison with SNPP advanced technology microwave sounder (ATMS) measurements. Through the community radiative transfer model (CRTM), brightness temperature (BT) at SNPP ATMS channels are simulated with COSMIC-2 retrieved atmospheric profiles from two versions of the University Corporation for Atmospheric Research (UCAR) wet profiles (WETprf and WETpf2) as inputs to the CRTM simulation. The analysis was focused on ATMS sounding channels CH07–14 and CH19–22 with sounding weighting function peak heights from 3.2 to 35 km. The COSMIC-2 vs. ATMS inter-comparison indicates that their BT biases are consistent, and the latitudinal difference is <0.3 K over three latitudinal regions. The differences between the two versions of UCAR COSMIC-2 wet profiles are identified and attributed to the differences in the implementation of 1DVAR retrieval algorithms. The stability between UCAR near real-time COSMIC-2 wet profile data and ATMS measurements is also well-maintained. It is demonstrated that the well-sustained quality of COSMIC-2 RO data makes itself a well-suited reference sensor to capture the calibration update of SNPP ATMS. Furthermore, the impacts of the assimilation of COSMIC-2 data into the European Centre for Medium-Range Weather Forecasts (ECMWF) model after 25 March 2020, are evaluated by trending observation-minus-background (O-B) biases, which confirms the statistically significant positive impacts of COSMIC-2 on the ECMWF reanalysis. The validation of stability and consistency between COSMIC-2 and SNPP ATMS ensures the quality of RO and microwave sounder data assimilated into the NWP models.

Performance analysis on wideband fast Fourier transform spectrometer of THz atmospheric limb Sounder (TALIS)

THz Atmospheric Limb Sounder (TALIS) is a terahertz band microwave radiometer used to sound the vertical distribution of the atmosphere, focusing on the distribution of trace gases. The back-end fast Fourier transform spectrometer (FFTS) is the core component of TALIS and will be used for the first time in the limb sounding. The radix 16 real-time complex FFT algorithm is the core algorithm of the FFTS. Each FFTS has a bandwidth of 2 GHz and a spectral resolution of 2.3 MHz. In this study, the improved error evaluation of the core algorithm and the multi-perspective analysis of the quantization bits are introduced. The results show that brightness temperature bias, with a maximum value of about 0.02 K, will be caused by the core algorithm. As the quantization bits decrease, the correlation of the power spectrum decreases and the brightness temperature biases at the peaks increase. Compared with the ideal quantization, the overall correlation coefficients of the power spectrum for 3-bit and greater quantization are above 0.9999, the maximum brightness temperature biases are approximately 0.08 K, 0.3 K, 1.2 K, 4.4 K, 9.5 K, and 21 K, corresponding to the 10-bit, 8-bit, 6-bit, 4-bit, 3-bit, and 2-bit quantization, and the 3-bit and 2-bit quantization will lead to a deterioration of TALIS sensitivity by about 2% and 12%, respectively. The research in this study will provide an important reference for the scientific design of TALIS, and it will provide vital practical significance for the implementation of more channels of the FFTS.

Observation error diagnostics and applicability evaluation of MHS brightness temperatures in RMAPS-CA

ABSTRACT Due to the insufficiencies of conventional observations and the effectiveness of bias corrections, it is still necessary to assimilate data from polar orbit satellites into a limited-area rapidly updated multiscale analysis and prediction system for Central Asia (RMAPS-CA). This study diagnoses the observation errors of the radiation brightness temperature (BT) of satellite Microwave Humidity Sounder (MHS) from multiple platforms through comparisons to the simulated BTs by the Community Radiative Transfer Model (CRTM) with soundings and a GFS input separately. The results show that the majority BIAS of the MHS BT compared with the simulated BT falls within −1.5–2.0 K and increases with an increase in the peak energy contribution height (PECH). No significant bias differences for the same channel of MHS and the same satellite are found across the different sounding stations, but obvious differences are found for the same channel from different satellites. Additionally, most of the GFS BTs are higher than the MHS BTs, which increase with the PECH. Moreover, almost all PDF distributions of the observed MHS BT bias relative to the GFS BT form a nearly Gaussian distribution, with the centre value being positive for channels 3 and 4. An assessment of the initial applications of MHS to RMAPS-CA is then conducted through two one-month retrospective runs, and forecasts initialized from analyses with and without MHS are verified against the observations. The results show that compared to the natural run for which no observations are assimilated, an improvement in the geopotential height of approximately 55.3% can be obtained by the assimilation of MHS, along with improvements of 12.8%, 4.7% and 2.3% for temperature, specific humidity (SPFH), and wind speeds below 200 hPa, respectively, for an average RMSE with initial time, which is valued at 2.9% and 3.3% for the SPFH at 2 m and wind speed at 10 m, respectively In addition, significant improvements in 24-h precipitation of 11.3%, 28.7% and 20.0% are obtained for light, medium and heavy rain, respectively, while no positive effects are obtained for rainstorm forecasts.

Evaluation of aqua MODIS thermal emissive bands stability through radiative transfer modeling

Moderate Resolution Imaging Spectroradiometer (MODIS) on Aqua has been in operation providing continuous global observations for science research and applications since 2002. The long-term stability of thermal emissive bands (TEBs) of Aqua MODIS was monitored through inter-comparisons with measurements by hyperspectral or multi-spectral infrared sensors or through vicarious monitoring over cold targets such as Dome-C and deep convective clouds. The radiative transfer modeling (RTM)-based simulation models are developed to perform the long-term monitoring of the stability of Aqua MODIS TEBs. Long-term European Centre for Medium-Range Weather Forecasts global atmospheric reanalysis data are used as inputs to the RTM simulation. By confining ocean in low to middle latitudes as the area of interest, the long-term stabilities of Aqua MODIS TEBs are monitored through observation minus background (O-B) brightness temperature (BT) bias between MODIS measurements and simulations from the RTM. In general, the RTM-based O-B analysis shows that the long-term stability of Aqua MODIS TEBs are maintained well. It is shown that Aqua MODIS surface bands are all radiometrically stable with yearly BT bias drifts <0.005 K / year for B20, B22, and B23 and ∼0.01 K / year for B31 and B32. The carbon dioxide (CO2) absorption channels of Aqua MODIS, e.g., B33 to B36, are stable with a BT bias drift <0.005 K / year. It is also found that after accounting for the long-term growth of greenhouse gas N2O, the O-B bias trends for B24 and B25 are quite stable with yearly BT bias drifts around 0.0002 and 0.0055 K / year, respectively. The stabilities of the moisture-sensitive channels B27 to B29 and the ozone channel B30 of Aqua MODIS are also evaluated and the remnant small variations in the O-B bias trending of these channels are further discussed. The RTM-based inter-comparison of MODIS TEB measurements with global atmospheric and climate re-analysis data provides validation of the long-term radiometric stability of Aqua MODIS TEBs. The analysis in this paper also demonstrates that the comparison of well-calibrated MODIS TEB measurements with RTM simulation helps quantify the long-term impacts of global greenhouse gas concentration growth on the BT decreases in the MODIS greenhouse gas-sensitive channels.

천리안2A호를 이용한 다목적실용위성3A호 중적외선 밝기 온도 편향오차 개선 가능성 연구

2015년 발사된 다목적실용위성 3A호는 3.3~5.2μm 파장의 중적외선 영상을 제공한다. 다목적실용위성 3A호는 지상 물체의 밝기 온도를 추정하기 위한 고해상도 영상을 제공하지만 기존 자연 과학 목적으로 개발된 위성과 차이가 있다. 다목적실용위성 3A호의 단일 채널의 중적외선 영상으로 지표 밝기 온도를 추정하기 위해서는 대기 보정 과정이 필수적이다. 하지만 대기 보정 이후에도 여러 요인으로 인하여 밝기 온도 추정 오차가 존재한다. 본 논문에서는 다목적실용위성 3A호 카메라의 물리적인 특성으로부터 영상 처리까지 신호 흐름을 추적하여 밝기 온도 추정 오차 요인을 분석하였다. 또한, 천리안위성 2A호를 이용하여 다목적실용위성 3A호와 밝기 온도 편향 오차 개선가능성을 연구하였다. 큰 편향 오차를 가지고 있는 야간 영상에 대하여 편향 오차를 보상한 이후 다목적실용위성 3A호와 천리안위성 2A호의 지표 밝기 온도가 상관성이 있음을 확인하였다. 다목적실용위성 3A호 중적외선 밝기 온도 편향 오차를 개선하는데 천리안위성 2A호 영상이 도움이 될 것으로 예상된다.

Harmonization of Space-Borne Infra-Red Sensors Measuring Sea Surface Temperature

Sea surface temperature (SST) is observed by a constellation of sensors, and SST retrievals are commonly combined into gridded SST analyses and climate data records (CDRs). Differential biases between SSTs from different sensors cause errors in such products, including feature artefacts. We introduce a new method for reducing differential biases across the SST constellation, by reconciling the brightness temperature (BT) calibration and SST retrieval parameters between sensors. We use the Advanced Along-Track Scanning Radiometer (AATSR) and the Sea and Land Surface Temperature Radiometer (SLSTR) as reference sensors, and the Advanced Very High Resolution Radiometer (AVHRR) of the MetOp-A mission to bridge the gap between these references. Observations across a range of AVHRR zenith angles are matched with dual-view three-channel skin SST retrievals from the AATSR and SLSTR. These skin SSTs act as the harmonization reference for AVHRR retrievals by optimal estimation (OE). Parameters for the harmonized AVHRR OE are iteratively determined, including BT bias corrections and observation error covariance matrices as functions of water-vapor path. The OE SSTs obtained from AVHRR are shown to be closely consistent with the reference sensor SSTs. Independent validation against drifting buoy SSTs shows that the AVHRR OE retrieval is stable across the reference-sensor gap. We discuss that this method is suitable to improve consistency across the whole constellation of SST sensors. The approach will help stabilize and reduce errors in future SST CDRs, as well as having application to other domains of remote sensing.

Empirical Characterization of the SMOS Brightness Temperature Bias and Uncertainty for Improving Sea Surface Salinity Retrieval

After more than eight years of the European Space Agency (ESA) Soil Moisture and Ocean Salinity (SMOS) acquisitions, an exhaustive, empirical characterization of the biases and uncertainties affecting SMOS brightness temperatures over the ocean is possible. We show that both parameters strongly depend not only on the position in the field of view, but also on the geographical location of the acquisition. Metrics based on the differences between expected and theoretical values of the bias and the uncertainty are developed and used for quantitatively assessing the locations where SMOS errors are currently not accurately characterized. This characterization can be used for the definition of a new empirical SMOS sea surface salinity (SSS) bias correction, a better cost function retrieval, and more accurate filtering criteria, which are expected to lead to a better SMOS SSS Level 2 product. We present a new L2 SMOS SSS product based on the described investigation. The performance of this preliminary product is similar to that of the version v662 of the official L2 SMOS SSS product at medium and low latitudes. However, it provides a better coverage at high latitudes and coastal regions affected by radio frequency interference (RFI), which correspond to those regions where the SMOS errors are currently poorly estimated.

Single-scattering properties of ice particles in the microwave regime: Temperature effect on the ice refractive index with implications in remote sensing

An ice crystal single-scattering property database is developed in the microwave spectral region (1 to 874GHz) to provide the scattering, absorption, and polarization properties of 12 ice crystal habits (10-plate aggregate, 5-plate aggregate, 8-column aggregate, solid hexagonal column, hollow hexagonal column, hexagonal plate, solid bullet rosette, hollow bullet rosette, droxtal, oblate spheroid, prolate spheroid, and sphere) with particle maximum dimensions from 2µm to 10mm. For each habit, four temperatures (160, 200, 230, and 270K) are selected to account for temperature dependence of the ice refractive index. The microphysical and scattering properties include projected area, volume, extinction efficiency, single-scattering albedo, asymmetry factor, and six independent nonzero phase matrix elements (i.e. P11, P12, P22, P33, P43 and P44). The scattering properties are computed by the Invariant Imbedding T-Matrix (II-TM) method and the Improved Geometric Optics Method (IGOM). The computation results show that the temperature dependence of the ice single-scattering properties in the microwave region is significant, particularly at high frequencies. Potential active and passive remote sensing applications of the database are illustrated through radar reflectivity and radiative transfer calculations. For cloud radar applications, ignoring temperature dependence has little effect on ice water content measurements. For passive microwave remote sensing, ignoring temperature dependence may lead to brightness temperature biases up to 5K in the case of a large ice water path.

An Assessment of SAPHIR Calibration Using Quality Tropical Soundings

AbstractThe Sondeur Atmosphérique du Profil d’Humidité Intertropicale par Radiométrie (SAPHIR) instrument on board the Megha-Tropiques (MT) platform is a cross-track, multichannel microwave humidity sounder with six channels near the 183.31-GHz water vapor absorption line, a maximum scan angle of 42.96° (resulting in a maximum incidence angle of 50.7°), a 1700-km-wide swath, and a footprint resolution of 10 km at nadir. SAPHIR L1A2 brightness temperature (BT) observations have been compared to BTs simulated by the radiative transfer model (RTM) Radiative Transfer for the Television and Infrared Observation Satellite (TIROS) Operational Vertical Sounder (RTTOV-10), using in situ measurements from radiosondes as input. Selected radiosonde humidity observations from the Cooperative Indian Ocean Experiment on Intraseasonal Variability in the Year (CINDY)–Dynamics of the Madden–Julian Oscillation (DYNAMO) campaign (September 2011–March 2012) were spatiotemporally collocated with MT overpasses. Although several sonde systems were used during the campaign, all of the sites selected for this study used the Vaisala RS92-SGPD system and were chosen in order to avoid discrepancies in data quality and biases.To interpret the results of the comparison between the sensor data and the RTM simulations, uncertainties associated with the data processing must be propagated throughout the evaluation. The magnitude of the bias was found to be dependent on the observing channel, increasing from 0.18 K for the 183.31 ± 0.2-GHz channel to 2.3 K for the 183.31 ± 11-GHz channel. Uncertainties and errors that could impact the BT biases were investigated. These can be linked to the RTM input and design, the radiosonde observations, the chosen methodology of comparison, and the SAPHIR instrument itself.

Assimilation of clear sky Atmospheric Infrared Sounder radiances in short-term regional forecasts using community models

Regional assimilation experiments of clear-sky Atmospheric Infrared Sounder (AIRS) radiances were performed using the gridpoint statistical interpolation three-dimensional variational assimilation system coupled to the weather research and forecasting model. The data assimilation system and forecast model used in this study are separate community models; it cannot be assumed that the coupled systems work optimally. Tuning was performed on the data assimilation system and forecast model. Components tuned included the background error covariance matrix, the satellite radiance bias correction, the quality control procedures for AIRS radiances, the forecast model resolution, and the infrared channel selection. Assimilation metrics and diagnostics from the assimilation system were used to identify problems when combining separate systems. Forecasts initiated from analyses after assimilation were verified with model analyses, rawinsondes, nonassimilated satellite radiances, and 24 h–accumulated precipitation. Assimilation of clear sky AIRS radiances showed the largest improvement in temperature and radiance brightness temperature bias when compared with rawinsondes and satellite observations, respectively. Precipitation skill scores displayed minor changes with AIRS radiance assimilation. The 00 and 12 coordinated universal time (UTC) forecasts were typically of better quality than the 06 and 18 UTC forecasts, possibly due to the amount of AIRS data available for each assimilation cycle.

Assessment and Correction of on-Orbit Radiometric Calibration for FY-3 VIRR Thermal Infrared Channels

FengYun-3 (FY-3) Visible Infrared Radiometer (VIRR), along with its predecessor, Multispectral Visible Infrared Scanning Radiometer (MVISR), onboard FY-1C&amp;D have had continuous global observation more than 14 years. This data record is valuable for weather prediction, climate monitoring, and environment research. Data quality is vital for satellite data assimilations in Numerical Weather Prediction (NWP) and quantitative remote sensing applications. In this paper, the accuracies of radiometric calibration for VIRR onboard FY-3A and FY-3B, in thermal infrared (TIR) channels, are evaluated using the Low Earth Orbit (LEO)-LEO simultaneous nadir overpass intercalibration method. Hyperspectral and high-quality observations from Infrared Atmosphere Sounding Instrument (IASI) onboard METOP-A are used as reference. The biases of VIRR measurements with respect to IASI over one-and-a-half years indicate that the TIR calibration accuracy of FY-3B VIRR is better than that of FY-3A VIRR. The brightness temperature (BT) measured by FY-3A/VIRR is cooler than that measured by IASI with monthly mean biases ranging from −2 K to −1 K for channel 4 and −1 K to 0.2 K for channel 5. Measurements from FY-3B/VIRR are more consistent with that from IASI, and the annual mean biases are 0.84 ± 0.16 K and −0.66 ± 0.18 K for channels 4 and 5, respectively. The BT biases of FY-3A/VIRR show scene temperature-dependence and seasonal variation, which are not found from FY-3B/VIRR BT biases. The temperature-dependent biases are shown to be attributed to the nonlinearity of detectors. New nonlinear correction coefficients of FY-3A/VIRR TIR channels are reevaluated using various collocation samples. Verification results indicate that the use of the new nonlinear correction can greatly correct the scene temperature-dependent and systematic biases.