Temperature Retrieval Research Articles

Evaluating Landsat-9 TIRS-2 calibrations and land surface temperature retrievals against ground measurements using multi-instrument spatial and temporal sampling along transects

High-resolution TIR data are essential for a wide range of studies, e.g., in environmental monitoring, urban effects, water stress, agricultural productivity, and land/water resources. Landsat series provides high-resolution TIR data, but data accuracy must be ensured to contribute to these fields. After the calibration problems of Landsat 8 (L8) TIRS due to a stray light effect, efforts were made in the design of Landsat 9 (L9) TIRS-2 to mitigate this effect. In any case, field calibration and validation activities are necessary to evaluate the accuracy of recently launched sensors. These activities require high-quality ground TIR radiance data that are representative at satellite spatial resolution and that quantify spatial–temporal fluctuations at the experimental sites concurrently to satellite overpasses. However, these requirements are not always accomplished in the literature.This paper provides vicarious calibrations of the L9 TIRS-2 data with accurate ground measurements acquired using multi-instrument spatial and temporal sampling in a rice paddy area from December 2021 to August 2023, in order to contribute to the calibration and validation of L9 TIRS-data.In March 1, 2023 L9 TIRS-2 scenes started to be reprocessed and calibration was updated. Both the original calibration and that after reprocessing were evaluated. Close results were obtained for band 10 (b10). When comparing satellite brightness temperatures and those determined from in situ LSTs, systematic differences of −0.3 K were shown, with root-mean-square differences (RMSDs) of 0.5 K. Results for band 11 showed a bias of −0.1 K for the original data and of 0.2 K after the reprocessing, with RMSDs of 0.6 K.The L2 LST product generated operationally from b10 data was also evaluated, together with alternative single-channel (SC) algorithms. A bias (RMSD) of 0.4 K (1.1 K) was obtained for the product. However, negative biases were shown both with the radiative transfer equation used on b10 radiances and the alternative SC algorithm.Finally, we validated six types of split-window (SW) algorithms applied to L9 TIRS-2 data before and after the reprocessing. A systematic difference of about −0.6 K between SW LST biases when using the reprocessed and original data was observed. In any case, for most of the algorithms with original and reprocessed data, LST biases and standard deviations were within the recommended threshold of 1 K, which proves the good performance of L9 TIRS-2 data to monitor LST at high spatial resolution.

Evaluation of Temperature and Humidity Profiles Retrieved from Fengyun-4B and Implications for Typhoon Assimilation and Forecasting

Fengyun-4B (FY-4B) is the first operational satellite from China’s latest generation of geostationary meteorological satellites. It is equipped with the Geostationary Interferometric Infrared Sounder (GIIRS), which is able to obtain highly accurate atmospheric temperature and humidity profiles through hyperspectral detection in long- and mid-wave infrared spectral bands. In this study, the accuracy of the FY-4B/GIIRS temperature and humidity profile retrievals over two months is evaluated using radiosonde observations and ERA5 reanalysis data. We go a step further to investigate the impact of the satellite retrievals on assimilation and forecasts for Typhoons Chaba and Ma-on in 2022. Results reveal that the root-mean-square difference (RMSD) for the FY-4B/GIIRS temperature and humidity profile retrievals were within 1 K and 1.5 g/kg, respectively, demonstrating high overall accuracy. Moreover, assimilating temperature and humidity profiles from FY-4B/GIIRS positively impacts model analysis and prediction, improving typhoon track and intensity forecasts. Additionally, improvements have been discovered in predicting precipitation, particularly with high-magnitude rainfall events.

Version 8 IMK–IAA MIPAS temperatures from 12–15 µm spectra: Middle and Upper Atmosphere modes

Abstract. Motivated by an improved European Space Agency (ESA) version of calibrated Michelson Interferometer for Passive Atmospheric Sounding (MIPAS) spectra (version 8.03), we have released version 8 of MIPAS temperatures and pointing information retrieved from 2005–2012 MIPAS measurements at 12–15 µm in the Middle Atmosphere (MA), Upper Atmosphere (UA) and Noctilucent Cloud (NLC) measurement modes. The Institute of Meteorology and Climate Research–Instituto de Astrofisica de Andalucia (IMK–IAA) retrieval processor in use considers non-local thermodynamic equilibrium (non-LTE) emission explicitly for each limb scan. This non-LTE treatment is essential to obtain accurate temperatures above the mid-mesosphere because at the altitudes covered, up to 115 km, the simplified climatology-based non-LTE treatment employed for the Nominal (NOM) measurements is insufficient. Other updates in MA/UA/NLC version 8 non-LTE temperature retrievals from previous data releases include more realistic atomic oxygen and carbon dioxide abundances, an updated set of spectroscopic data, an improved spectral shift retrieval, a continuum retrieval extended to altitudes up to 58 km, consideration of an altitude-dependent radiance offset retrieval, the use of wider microwindows above 85 km to capture the offset, an improved accuracy in forward model calculations, new a priori temperature information, improved temperature horizontal gradient retrievals and the use of MIPAS version 5 interfering species where available. The resulting MIPAS MA/UA/NLC IMK–IAA temperature dataset is reliable for scientific analysis in the full measurement vertical range for the MA (18–102 km) and the NLC (39–102 km) observations and from 42 to 115 km for the UA observations. The random temperature errors, dominated by the instrumental noise, are typically less than 1 K below 60 km, 1–3 K at 60–70 km, 3–5 K at 70–90 km, 6–8 K at 90–100 km, 8–12 K at 100–105 km and 12–20 K at 105–115 km. Random pointing correction errors, also mainly arising from instrumental noise, are on average 50 m for tangent altitudes up to 60 km and decrease linearly to values smaller than 20 m for altitudes above 95 km. The vertical resolution is 3 km at altitudes below 50 km, 3–5 km at 50–70 km, 4–6 km at 70–90 km, 6–10 km at 90–100 km and 8–11 km at 100–115 km. The systematic errors in retrieved temperatures below 75 km are driven by uncertainties in the CO2 spectroscopic data and, above 80 km, by uncertainties in the non-LTE model parameters (including collisional rates and atomic oxygen abundance) and the CO2 abundance. These lead to systematic temperature errors of less than 0.7 K below 55 km, 1 K at 60–80 km, 1–2 K at 80–90 km, 3 K at 95 km, 6–8 K at 100 km, 10–20 K at 105 km and 20–30 K at 115 km. Systematic errors in the tangent altitude correction, mainly arising from CO2 spectroscopic uncertainties, are 250 m at 20 km, 200 m at 40–60 km, 100 m at 80 km and smaller than 50 m above 90 km. The consistency between the MA/UA/NLC and the NOM IMK–IAA datasets is excellent below 70 km (typical 0.5–1 K differences). The comparison of this temperature dataset with co-located Sounding of the Atmosphere using Broadband Emission Radiometry (SABER) temperature measurements shows excellent agreement, with differences typically within 1.5 K below 90 km, 1–3 K at 90–95 km, 1–5 K at 95–100 km, 1–8 K at 100–105 km and 1–10 K above. The agreement with SABER improves with respect to previous MIPAS IMK–IAA data versions.

Satellite-Based Optimization and Planning of Urban Ventilation Corridors for a Healthy Microclimate Environment

Urban ventilation corridors (UVCs) have the potential to effectively mitigate urban heat islands and air pollution. Shanghai, a densely populated city located in eastern China, is among the hottest cities in the country and requires urgent measures in order to enhance its ventilation system. This study introduces a novel approach that integrates land surface temperature retrieval, PM2.5 concentration retrieval, and wind field simulation to design UVCs at the city level. Through remote sensing data inversion of land surface temperature (LST) and PM2.5 concentration, the study identifies the action spaces and compensation spaces for UVCs. The Weather Research and Forecasting (WRF) model, coupled with the multilayer urban scheme Building Effect Parameterization (BEP) model, is employed to numerically simulate and analyze the wind field. Based on the identification of thirty high-temperature zones and high PM2.5 concentration zones as action spaces, and twenty-two low-temperature zones and low PM2.5 concentration zones as compensation spaces in Shanghai, the study constructs seven first-class ventilation corridors and nine secondary ventilation corridors according to local circulation patterns. Unlike previous UVC research, this study assesses the cleanliness of cold air, which is a common oversight in UVC planning. Ignoring the assessment of cold air cleanliness can result in less effective UVCs in improving urban air quality and even exacerbate air pollution in the central city. Therefore, this study serves as a crucial contribution by rectifying this significant deficiency. It not only provides a fresh perspective and methodology for urban-scale ventilation corridor planning but also contributes to enhancing the urban microclimate by mitigating the effects of urban heat islands and reducing air pollution, ultimately creating a livable and comfortable environment for urban residents.

An automatic extraction method for geothermal radiation sources based on an LST retrieval algorithm and semantic network

Geothermal resources are efficient, renewable and clean energy sources, and their reservoirs are usually closely associated with high-temperature regions of the land surface. Current exploration methods primarily involve migrating traditional geological techniques, which fail to fully use the unique features of geothermal radiation characteristics. Thermal infrared remote-sensing imaging technology can capture and present areas with distinctive surface thermal radiation features, providing considerable significance as a guide for localization prior to field exploration. In this study, we propose a deep learning–based method for intelligently identifying and segmenting geothermal radiation sources from thermal infrared remote-sensing images, including data preparation and model training. To improve the localization drift and anomalous interference caused by the high complexity of the Earth's surface environment, this study uses a surface temperature retrieval algorithm to calculate the land surface temperature in the research area. The retrieval results are used to train the semantic segmentation model. In addition, a pixel-level geothermal spatial segmentation network (PGSSNet) is proposed to suppress the diffuse thermal radiation and reduce the broad and blurred white areas of images to exact locations. Once the training is completed, the model directly segments and extracts the actual range of thermal radiation sources from subsequent thermal infrared remote-sensing images without temperature retrieval and/or manual calibration.

Retrieval of temperature and humidity profiles from ground-based high-resolution infrared observations using an adaptive fast iterative algorithm

Abstract. Various retrieval algorithms have been developed for retrieving temperature and water vapor profiles from Atmospheric Emitted Radiance Interferometer (AERI) observations. The physical retrieval algorithm, named AERI Optimal Estimation (AERIoe), outperforms other retrieval algorithms in many aspects except the retrieval time, which is significantly increased due to the complex radiative transfer process. The calculation of the Jacobian matrix is the most computationally intensive step of the physical retrieval algorithm. Interestingly, an analysis of the change in AERI observations' information content with respect to Jacobians revealed that the AERIoe algorithm's performance presents negligible dependence on these metrics. Thus, the Jacobian matrix could remain unchanged when the variation in the atmospheric state is small in the retrieval process to reduce the most time-consuming computation. On the basis of the above findings, a fast physical–iterative retrieval algorithm was proposed by adaptively recalculating Jacobians in keeping with the changes in the atmospheric state. Experiments with synthetic observations demonstrate that the proposed method experiences an average reduction in retrieval time by an impressive 59 % compared to the original AERIoe algorithm while achieving maximum root-mean-square errors of less than 0.95 K and 0.22 log(ppmv) for heights below 3 km for the temperature and water vapor profile, respectively. Further analyses revealed that the fast-retrieval algorithm reached an acceptable convergence rate of 98.7 %, marginally lower than AERIoe's 99.9 % convergence rate for the 826 cases used in this study.

A new Einstein coefficient method for mesopause-lower thermosphere atmosphere temperature retrieval under a non-local thermal equilibrium situation.

The mesopause-lower thermosphere (MLT) region is an important spatial region in the Earth's atmosphere, making it a valuable area to investigate the temperature variations. Kirchhoff's law fails with the altitude increase due to the non-local thermal equilibrium effect, resulting in an increase in the error of the method to retrieve the atmospheric temperature in the MLT region using the A-band spectral line intensity. In the non-LTE state, the temperature retrieval method based on the Einstein coefficients is proposed to retrieve atmospheric temperature in the 92-140 km height range using the airglow radiation intensity images obtained from the Michelson Interferometer for global high-resolution thermospheric imaging (MIGHTI) measurements. Results show that the temperature deviation of the two-channel combinations does not exceed 15 K in the altitude range of 92-120 km. This deviation increases up to 45 K when the altitude is in the range of 120-140 km due to the influence of the N2 airglow spectrum. The two-channel combinations self-consistency is increased by 85 K compared with the temperature obtained using the spectral line intensity retrieval. Additionally, the comparison of the retrieval results with the spectral line intensity method and the comparison with the atmospheric chemistry experiment Fourier transform spectrometer (ACE-FTS) temperature measurement data shows that the Einstein coefficient method is significantly more rational and accurate than the spectral line intensity method.

Analysis of the adjacency effect on retrieval of land surface temperatures based on multimodal images from unmanned aerial vehicles

Land surface temperatures (LSTs) critically govern urban thermal environments. While algorithms have been proposed to retrieve LSTs from high-spatial-resolution images derived from unmanned aerial vehicles, efforts to explore urban geometry and adjacency effects on such retrieved LSTs are faced with challenges. In this study, a new urban radiative transfer model was proposed, which incorporated the emitted radiance from a target pixel and the atmosphere, the scattered radiance of the target pixel itself and its neighboring pixels, and the reflected atmospheric downwelling radiance. Based on the model and UAV-derived orthophotos, urban LSTs that considered the contributions of the surroundings were accurately retrieved. Depending on the emissivity and the surroundings of the target pixel, the magnitude of the adjacency effect on retrieved LSTs almost varied from 0.0 K to 3.0 K. Validation based on in-situ LSTs demonstrated that the absolute biases between the retrieved and measured temperatures were below 1.0 K. These findings have collectively underlined the necessity to consider the adjacency effect for the retrieval of urban LSTs from high-spatial-resolution images.

Comparative assessment of the accuracies of daytime land surface temperature retrieval methods using Landsat 8 and MODIS imageries in Benin, West Africa

Comparative assessment of the accuracies of daytime land surface temperature retrieval methods using Landsat 8 and MODIS imageries in Benin, West Africa

Machine Learning Model-Based Retrieval of Temperature and Relative Humidity Profiles Measured by Microwave Radiometer

The accuracy of temperature and relative humidity (RH) profiles retrieved by the ground-based microwave radiometer (MWR) is crucial for meteorological research. In this study, the four-year measurements of brightness temperature measured by the microwave radiometer from Huangpu meteorological station in Guangzhou, China, and the radiosonde data from the Qingyuan meteorological station (70 km northwest of Huangpu station) during the years from 2018 to 2021 are compared with the sonde data. To make a detailed comparison on the performance of machine learning models in retrieving the temperature and RH profiles, four machine learning algorithms, namely Deep Learning (DL), Gradient Boosting Machine (GBM), Extreme Gradient Boosting (XGBoost) and Random Forest (RF), are employed and verified. The results show that the DL model performs the best in temperature retrieval (with the root-mean-square error and the correlation coefficient of 2.36 and 0.98, respectively), while the RH of the four machine learning methods shows different excellence at different altitude levels. The integrated machine learning (ML) RH method is proposed here, in which a certain method with the minimum RMSE is selected from the four methods of DL, GBM, XGBoost and RF for a certain altitude level. Two cases on 29 January 2021 and on 10 February 2021 are used for illustration. The case on 29 January 2021 illustrates that the DL model is suitable for temperature retrieval and the ML model is suitable for RH retrieval in Guangzhou. The case on 10 February 2021 shows that the ML RH method reaches over 85% before precipitation, implying the application of the ML RH method in pre-precipitation warnings.

Evaluating European ECOSTRESS Hub Evapotranspiration Products Across a Range of Soil‐Atmospheric Aridity and Biomes Over Europe

AbstractThe ECOsystem Spaceborne Thermal Radiometer Experiment on Space Station (ECOSTRESS) is a scientific mission that collects high spatio‐temporal resolution (∼70 m, 1–5 days average revisit time) thermal images since its launch on 29 June 2018. As a predecessor of future missions, one of the main objectives of ECOSTRESS is to retrieve and understand the spatio‐temporal variations in terrestrial evapotranspiration (ET) and its responses to soil water availability and atmospheric aridity. In the European ECOSTRESS Hub (EEH), by taking advantage of land surface temperature (LST) retrievals, we generated ECOSTRESS ET products over Europe and Africa using three models with different structures and parameterization schemes, namely Surface Energy Balance System (SEBS) and Two Source Energy Balance (TSEB) parametric models, as well as the non‐parametric Surface Temperature Initiated Closure (STIC) model. A comprehensive evaluation of the EEH ET products was conducted with respect to flux measurements from 19 eddy covariance sites in Europe over six different biomes with diverse aridity levels. Results revealed comparable performances of STIC and SEBS (RMSE of ∼70 W m−2). However, the relatively complex TSEB model produced a higher RMSE of ∼90 W m−2. Comparison between STIC ET estimates and the operational ECOSTRESS ET product from NASA PT‐JPL model showed a larger RMSE (around 50 W m−2 higher) for the PT‐JPL ET estimates. Substantial overestimation (>80 W m−2) in PT‐JPL ET estimates was evident over shrublands and savannas, presumably due to weak constraint of LST in the model. Overall, the EEH supports ET retrieval for the future high‐resolution thermal missions.

Retrieval of Sea Surface Skin Temperature from the High Resolution Picture Transmission Data of the National Oceanic and Atmospheric Administration Series Satellites

The High Resolution Picture Transmission (HRPT) data of the National Oceanic and Atmospheric Administration (NOAA) series meteorological satellites had been received by the SeaSpace ground station located at the Ocean University of China (OUC). Based on the atmospheric radiative transfer model, we obtained the NOAA-15/16/17/18/19 Advanced Very High Resolution Radiometer (AVHRR) sea surface skin temperature (SSTskin) data using the Bayesian cloud detection method and the optimal estimation (OE) sea surface temperature (SST) retrieval algorithm. Compared with the NOAA/AVHRR multi-channel SST data, the AVHRR SSTskin data have higher data accuracy. We also compared the AVHRR SSTskin with the buoy SST with spatial and temporal windows of 0.01° and 30 min. The daytime biases ranged from −0.32 °C (NOAA-16) to 0.08 °C (NOAA-17) with standard deviations (SDs) ranging from 0.36 °C (NOAA-18/ NOAA-19) to 0.60 °C (NOAA-16), and the nighttime biases ranged from −0.26 °C (NOAA-16) to −0.02 °C (NOAA-17) with SDs ranging from 0.33 °C (NOAA-19) to 0.60 °C (NOAA-16). The accuracy of all five satellite data during daytime and nighttime was significantly improved. These results show that the AVHRR SSTskin of NOAA series satellites is good and consistent in different periods, and the SSTskin data products with high spatial resolution and accuracy can be used for mesoscale and submesoscale marine applications.

Effects of the Hunga Tonga‐Hunga Ha'apai Eruption on MODIS‐Retrieved Sea Surface Temperatures

AbstractThe eruption of Hunga Tonga‐Hunga Ha'apai (HTHH) volcano on 15 January 2022 injected a great amount of H2O and a moderate amount of SO2 into the stratosphere, producing a pronounced and persistent sulfate aerosol layer centered around the mid‐stratosphere, mostly confined to Southern Hemisphere (SH) tropics. These aerosols affect the Moderate Resolution Imaging Spectroradiometer (MODIS) retrievals of sea surface temperature (SST) where negative biases reached −0.3 K and an annual mean of −0.1 K north of 40°S in the SH. The spatial and temporal evolutions of MODIS SST anomalies are presented. Radiative transfer simulations demonstrate the aerosol effect on MODIS SST retrievals by causing an additional brightness temperature (BT) deficit at 11 μm and a reduction in BT differences since the characteristic of spectral attenuation between 11 and 12 μm is opposite to that of H2O. A correction for HTHH aerosol effects in the retrieval algorithm is therefore desirable.

Machine Learning and VIIRS Satellite Retrievals for Skillful Fuel Moisture Content Monitoring in Wildfire Management

Monitoring the fuel moisture content (FMC) of 10 h dead vegetation is crucial for managing and mitigating the impact of wildland fires. The combination of in situ FMC observations, numerical weather prediction (NWP) models, and satellite retrievals has facilitated the development of machine learning (ML) models to estimate 10 h dead FMC retrievals over the contiguous US (CONUS). In this study, ML models were trained using variables from the National Water Model, the High-Resolution Rapid Refresh (HRRR) NWP model, and static surface properties, along with surface reflectances and land surface temperature (LST) retrievals from the Visible Infrared Imaging Radiometer Suite (VIIRS) instrument on the Suomi-NPP satellite system. Extensive hyper-parameter optimization resulted in skillful FMC models compared to a daily climatography RMSE (+44%) and an hourly climatography RMSE (+24%). Notably, VIIRS retrievals played a significant role as predictors for estimating 10 h dead FMC, demonstrating their importance as a group due to their high band correlation. Conversely, individual predictors within the HRRR group exhibited relatively high importance according to explainability techniques. Removing both HRRR and VIIRS retrievals as model inputs led to a significant decline in performance, particularly with worse RMSE values when excluding VIIRS retrievals. The importance of the VIIRS predictor group reinforces the dynamic relationship between 10 h dead fuel, the atmosphere, and soil moisture. These findings underscore the significance of selecting appropriate data sources when utilizing ML models for FMC prediction. VIIRS retrievals, in combination with selected HRRR variables, emerge as critical components in achieving skillful FMC estimates.

Retrieval and validation of the dawn-dusk land surface temperature from FY-3E MERSI-LL

ABSTRACT Land surface temperature (LST) is an important physical parameter that reflects changes in surface processes. Remote sensing makes it possible to obtain global or regional LSTs with high spatial and temporal resolution. Feng-Yun 3E (FY-3E) is the world’s first civilian dawn-dusk orbiting meteorological satellite on board the Medium Resolution Spectral Imager – Low-Light (MERSI-LL) sensor capable of acquiring LST at 250 m spatial resolution at dawn and dusk. By complementing with existing polar-orbiting satellites, FY-3E can fill the satellite observation gap within the 6-hour assimilation window and further enhance the observation capability of LST. To retrieve accurate LST from MERSI-LL, the generalized split-window (GSW) method was developed. The GSW coefficients were derived from the simulation dataset produced by Moderate Resolution Transmittance Code 5.2 with an atmospheric profile database. As a key input to the GSW algorithm, the land surface emissivity was dynamically estimated by the Advanced Spaceborne Thermal Emission and Reflection Radiometer Global Emissivity Dataset in combination with the fractional vegetation cover. The water vapour content is calculated in real time by the split-window covariance/variance ratio method. The comparison between retrieved LSTs and in situ LSTs shows that the GSW algorithm can effectively retrieve the LSTs of vegetation cover surfaces. The root mean square error ranged from 0.79 to 1.79 K at dawn and from 1.32 to 1.92 K at dusk.

Adapting an Existing Empirical Algorithm for Microwave Land Surface Temperature Retrieval in China for AMSR2 Data

To extend the time span of the microwave (MW) land surface temperature (LST) dataset in China, this study proposed an optimized empirical algorithm for Advanced Microwave Scanning Radiometer 2 (AMSR2) LST retrieval based on the algorithm for its predecessor, the AMSR-Earth Observing System (AMSR-E). A modified comprehensive classification system of environmental variables (CCSEV) that considered the impact of landform, landcover, atmospheric conditions, and solar radiation on the variation of LST was first constructed, and the LST for each class in the CCSEV was then retrieved through stepwise regression using the brightness temperature in different AMSR2 channels. The results indicate that the annual RMSE of the AMSR2 LST, compared to the reference Moderate Resolution Imaging Spectroradiometer (MODIS) LST from 2012 to 2020, varies between 3.26 K and 3.61 K in the daytime and 2.76 K and 2.96 K in the nighttime, respectively. The RMSE of the AMSR2 LST compared to the field measurements at the sites of the Beidahe river basin and Naqu regions varies between 4.16 K and 5.26 K in the daytime and 2.4 K and 5.17 K in the nighttime. The accuracy is relatively low in the warmer months and daytime due to the stronger solar radiation, and is also relatively low in western China due to the dominate highly fluctuating topography and barren and arid landcover. Generally, the accuracy of the AMSR2 LST is comparable with that of the AMSR-E LST retrieved by the predecessor algorithm, which facilitates coherent long-term applications using AMSR series LST datasets.

Retrieval of cloudy land surface temperature from AMSR2 brightness temperature, precipitable water vapour and cloud liquid water

ABSTRACT Land surface temperature (LST) is an important parameter in many studies including hydrology, meteorology, and ecology. In addition to thermal infrared remote sensing, passive microwave remote sensing is an effective alternative for LST retrieval, particularly under cloudy conditions. In this study, we analysed the impact of cloud on the transfer of microwave radiation in simulated database and presented a parameterization method to compensate the impact. On this basis, we put forward an improved method for LST retrieval from Advanced Microwave Scanning Radiometer 2 (AMSR2) brightness temperature at the 18.7 and 23.8 GHz channels with precipitable water vapour (PWV) and cloud liquid water (CLW) as supplementary input. The method is built on the basis of microwave radiative transfer equation and the assumption that 1) surface emissivity at two adjacent channels is linear and 2) the main factor causing brightness temperature difference at two channels is the absorption difference of PWV and CLW. The LST retrieval method was tested in simulated database, with root mean square error (RMSE) of 3.1 K. The method was then applied to AMSR2 brightness temperature and GFS reanalysis data to estimate global LST in 2019–2020, and the accuracy was validated using in situ LST measurements collected from 52 sites. The RMSE is approximately 4.7 K and 3.7 K for daytime and night-time, respectively. The improved LST retrieval method is promising in long-temporal and large-area applications.

Retrieval of oceanic chlorophyll concentration from GOES-R Advanced Baseline Imager using deep learning

Data of chlorophyll (Chl-a) concentration obtained from polar-orbiting ocean color satellite sensors are subject to major spatial and temporal gaps in data coverage owing to the intrinsically low sampling frequency that coincides with adverse conditions such as cloud obstruction. Such gaps can be minimized with geostationary satellites owing to their higher scanning frequency over a given region, allowing more sampling opportunities as clouds move. However, geostationary ocean color missions have not been available for most regions of the globe. The Advanced Baseline Imager (ABI) aboard the Geostationary Operational Environmental Satellite R series (GOES-R) is a potential alternative for the U.S. owing to improved signal-to-noise ratio compared with its predecessors. Nonetheless, retrieving [Chl-a] from ABI measurements is challenging; the number, placement, and width of its spectral bands are suboptimal and an “ocean-grade” atmospheric correction scheme is lacking. Here we describe the construction of a deep learning-based algorithm to derive [Chl-a] from GOES-16 ABI L1B radiance and ancillary data without an explicit atmospheric correction algorithm. A large (∼22 million examples) training dataset of paired ABI observations and [Chl-a] estimates derived from the Visible Infrared Imaging Radiometer Suite (VIIRS) on the Suomi National Polar-orbiting Partnership (SNPP) were used to train, test and validate a 10-layer deep neural network model to retrieve [Chl-a] from ABI data. Preliminary results show that without atmospheric correction but with the aid of deep learning, it is possible to detect open ocean features with strong contrast such as across eddies and fronts, but there are still significant issues in coastal waters and occasionally in the open oceans. In general, the ABI-derived [Chl-a] agrees reasonably well with VIIRS-derived values in the open ocean during 3–5 h of the day. The location of oceanographic features such as fronts and eddies in the ABI-derived [Chl-a] maps are also coincident with those obtained from traditional ocean color and sea surface temperature retrievals. The findings of this study demonstrate that deep learning is a powerful tool for capturing intricate patterns that are too subtle for traditional algorithms developed by humans, Furthermore, the utilization of GOES-R ABI has the potential to complement polar-orbiting satellite data and mitigate data gaps.

Retrieval of atmospheric temperature and humidity profiles over a tropical coastal station from ground-based Microwave Radiometer using deep learning technique

Ground-based multi-frequency microwave radiometer Profiler (Microwave Radiometer Profiler-MRP) provides valuable information on the altitude distribution of atmospheric humidity and temperature in the troposphere (upto ∼10 km) with high temporal resolution (of ∼1 min) under all-weather condition. These information are vital inputs for characterization of the atmospheric boundary layer, convective cloud systems, local weather modification and for studying the atmospheric dynamics. Potential of such radiometers are well established over the mid-latitude region. Thiruvananthapuram, located in the Indian peninsular region, is one of the first stations in the equatorial region where such an instrument made continuously operational for seven years since 2010 at the tropical coastal station. The instrument measures the brightness temperatures (Tb in Kelvin) at Ka- and V-band frequencies with a temporal resolution of ∼1 min. A trained back-propagation neural network technique based on the radiosonde ascends data for more than one decade obtained by India Meteorological station at Thiruvananthapuram, was used to derive the atmospheric humidity and temperature profiles. This paper presents the implementation of another retrieval technique based on deep learning approach - batch normalization and neural network (BNN) to retrieve temperature and water vapour density (WVD) profiles from Tb values during clear sky conditions and validation of the retrieval accuracies. This inversion model, much faster in computation, consists of two hidden layers and the rectified linear unit (ReLU) is used as the activation function as it can overcome the problems of saturation and vanishing gradients. The Tbs observed by radiometer are corrected by reducing the bias between the simulated Tb, using forward radiative transfer model, and the observed Tb. The validation of retrieved profiles with the radiosonde demonstrates a good retrieval capability, showing a root-mean-square error of 1.8K for temperature, <2 g/m3 for WVD and ∼14% for RH.

Explicitly determined sea ice emissivity and emission temperature over the Arctic for surface‐sensitive microwave channels

AbstractData assimilation of satellite microwave measurements is one of the important keys to improving weather forecasting over the Arctic region. However, the use of surface‐sensitive microwave‐sounding channel measurements for data assimilation or retrieval has been limited, especially during winter, due to the poorly constrained sea ice emissivity. In this study, aiming at more use of those channel measurements in the data assimilation, we propose an explicit method for specifying the surface radiative boundary conditions (namely emissivity and emitting layer temperature of snow and ice). These were explicitly determined with a radiative transfer model for snow and ice and with snow/ice physical parameters (i.e. snow/ice depths and vertical distributions of temperature, density, salinity, and grain size) simulated from the thermodynamically driven snow/ice growth model. We conducted 1D‐Var experiments in order to examine whether this approach can help to use the surface‐sensitive microwave temperature channel measurements over the Arctic sea ice region for data assimilation. Results show that (1) the surface‐sensitive microwave channels can be used in the 1D‐Var retrieval, and (2) the specification of the radiative boundary condition at the surface using the snow/sea ice emission model can significantly improve the atmospheric temperature retrieval, especially in the lower troposphere (500 hPa to surface). The successful retrieval suggests that useful information can be extracted from surface‐sensitive microwave‐sounding channel radiances over sea ice surfaces through the explicit determination of snow/ice emissivity and emitting layer temperature.