Thermodynamic Initial Guess Retrieval Research Articles

Retrieval of land surface temperature from FY3D MERSI-II based on re-fitting Split Window Algorithm

ABSTRACT Medium Resolution Spectral Imager II (MERSI-II) is one of the core sensors mounted on the FengYun-3D (FY3D) satellite. Two adjacent 250 m long-wave thermal infrared (TIR) channels provide a considerable opportunity for retrieving Land Surface Temperature (LST) with high spatiotemporal resolution. In this paper, Thermodynamic Initial Guess Retrieval (TIGR) dataset and MODTRAN 4.0 model were used to re-fit the parameters of the Split-Window (SW) algorithm suitable for MERSI-II TIR channels, and then the daily 250 m resolution MERSI-II LST product was retrieved. The Radiance-based (R-based) method results showed that the bias value between simulated by MODTRAN4.0 and the input is 0.16 K, and the MAE value is 0.38 K. Inter-comparison method results showed that the MERSI-II LST and MODIS LST products were consistent in spatial distribution, but there were certain differences between MODIS LST and MERSI-II LST at different land cover types. T-based method results showed that R values between the site-observed LST and MERSI-II LST retrieved by SW algorithm exceeded 0.92, the bias value was between 3.6 K and 4.4 K, and the MAE value was between 2.6 K and 4.5 K. The above results indicating that the SW algorithm proposed in this study has good accuracy and applicability.

Computation of longwave radiative flux and vertical heating rate with 4A-Flux v1.0 as an integral part of the radiative transfer code 4A/OP v1.5

Abstract. Based on advanced spectroscopic databases, line-by-line and layer-by-layer radiative transfer codes numerically solve the radiative transfer equation with very high accuracy. Taking advantage of its pre-calculated optical depth lookup table, the fast and accurate radiative transfer model Automatized Atmospheric Absorption Atlas OPerational (4A/OP) calculates the transmission and radiance spectra for a user-defined layered atmospheric model. Here, we present a module called 4A-Flux, which is developed and implemented into 4A/OP in order to include the calculation of the clear-sky longwave radiative flux profiles and heating rate profiles at a very high spectral resolution. Calculations are performed under the assumption of local thermodynamic equilibrium, a plane-parallel atmosphere, and specular reflection on the surface. The computation takes advantage of pre-tabulated exponential integral functions that are used instead of a classic angular quadrature. Furthermore, the sub-layer variation of the Planck function is implemented to better represent the emission of layers with a high optical depth. Thanks to the implementation of 4A-Flux, 4A/OP models have participated in the Radiative Forcing Model Intercomparison Project (RFMIP-IRF) along with other state-of-the-art radiative transfer models. 4A/OP hemispheric flux profiles are compared to other models over the 1800 representative atmospheric situations of RFMIP, yielding an outgoing longwave radiation (OLR) mean difference between 4A/OP and other models of −0.148 W m−2 and a standard deviation of 0.218 W m−2, showing a good agreement between 4A/OP and other models. 4A/OP is applied to the Thermodynamic Initial Guess Retrieval (TIGR) atmospheric database to analyze the response of the OLR and vertical heating rate to several perturbations of temperature or gas concentration. This work shows that 4A/OP with 4A-Flux module can successfully be used to simulate accurate flux and heating rate profiles and provide useful sensitivity studies including sensitivities to minor trace gases such as HFC134a, HCFC22, and CFC113. We also highlight the interest for the modeling community to extend intercomparison between models to comparisons between spectroscopic databases and modeling to improve the confidence in model simulations.

A Simplified Approach to Retrieve the K-Band Microwave Surface Emissivity Under Clear Skies

Microwave land surface emissivity (MLSE) at the K band plays a key role in driving geophysical parameters, such as land surface temperature (LST). However, satellite-based MLSE currently is hard to be quickly retrieved in clear skies since the time cost is high in removing atmospheric contributions. In this letter, one clear-sky atmospheric profile dataset, including a wide range of precipitable water vapor (PWV) values, was constructed using the Thermodynamic Initial Guess Retrieval database for analyzing numerical relationships between PWV and atmospheric parameters. Then, a simplified algorithm was developed for accurately retrieving instantaneous K-bandMLSEs (18.7 and 23.8 GHz) under clear skies, which can significantly save the time of atmospheric correction. The sensitivity analysis shows that PWV is a key factor affecting MLSE estimation at 23.8 GHz, and the brightness temperature (BT) uncertainty has a greater impact on MLSE estimation than LST. Additionally, with LST derived from the Moderate-resolution Imaging Spectroradiometer, BT from the Advanced Microwave Scanning Radiometer Earth Observing System (AMSR-E), and the ERA5 reanalysis PWV in 2008, the proposed algorithm was respectively applied in Europe and the United States for presenting its applicability at a station scale and regional scale. The actual sounding profile and global AMSR-E MLSE product were used as validation datasets. Results indicate the simplified approach has a good performance with RMSEs less than 0.02 in the site and regional validations. Whereas, there are some apparent overestimations in estimating clear-skies MLSEs, especially for 23.8 GHz. We believe the proposed approach is promising for retrieving other parameters.

Retrieving Land Surface Temperature From Chinese FY-3D MERSI-2 Data Using an Operational Split Window Algorithm

Medium resolution spectral imager II (MERSI-2) is a payload for the Chinese meteorological satellite Feng Yun 3 (FY-3). China's satellite remote sensing observation capabilities such as climate change research can be improved during MERSI-2's operation in orbit, and the sensor is the world's first imaging instrument that can obtain a global infrared split-window data with a spatial resolution of 250 m. We developed an operational spilt-window (SW) algorithm to retrieve land surface temperature (LST) accurately from the MERSI-2 data. The SW algorithm coefficients were derived from a simulation dataset that was established with the Moderate spectral resolution atmospheric Transmittance model version 5.2 and the thermodynamic initial guess retrieval dataset. In the practical retrieval, the precise algorithm coefficients were determined by view zenith angle and atmospheric water vapor content (WVC), the atmospheric WVC were obtained from the ERA5 dataset, and the land surface emissivity was dynamically estimated using the advanced spaceborne thermal emission and reflection radiometer global emissivity dataset, considering the fractional of vegetation cover and snow cover. The retrieved LST compared with in situ LST, which was highly consistent with the in situ LST and that the root-mean-square error of the two is within 3 K. The retrieved LST was compared with the MYD11_L2 and MYD21_L2 LST products, and the results indicated that MERSI-2 LST was more consistent with the MYD21 LST. The operational SW algorithm for FY-3D MERSI-2 developed in this study could retrieve LST accurately and has a wide range of popularization and application values.

Impact of Air Temperature Inversion on the Clear-Sky Surface Downward Longwave Radiation Estimation

Parameterization schemes for estimating clear-sky surface downward longwave radiation (SDLR) are well recognized for their simplicity and acceptable accuracy, especially at the local scale. The near-surface temperature and/or water vapor are usually used to predict the clear-sky SDLR in a parameterization scheme. Air temperature inversion (ATI) alters the atmospheric state at the near-surface boundary layer and affects the accuracy of the clear-sky SDLR estimation. However, few studies have investigated the impact of ATI on the estimate of the clear-sky SDLR. This article investigated the impact of ATI on the estimate of the clear-sky SDLR using six widely used parameterization schemes. According to the evaluation results using ATI profiles from the Thermodynamic Initial Guess Retrieval (TIGR) database and the Surface Radiation Budget Network (SURFRAD) sites, all the parameterization schemes are sensitive to ATI, and their accuracy is degraded greatly as a whole. The SDLR is underestimated for the ATI profile both in the TIGR database and SURFRAD sites. The best three schemes can achieve the accuracy with bias values of approximately -10 W/m <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> and root-mean-square errors (RMSEs) less than 20 W/m <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> for the ATI profiles in the TIGR database. The reason the SDLR is underestimated for the ATI profiles is provided by a simulation study. An empirical method is proposed to correct the impact of ATI. The accuracy of all the parameterization schemes is remarkably improved at SURFRAD sites after correcting the impact of ATI, with the absolute values of bias and RMSEs less than 10 and 20 W/m <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> at SURFRAD sites.

Estimation of Land Surface Temperature From MODIS Data for the Atmosphere With Air Temperature Inversion Profile

Air temperature inversion (ATI), which usually occurs at the near surface boundary layer in the atmosphere, influences the thermal path atmospheric upwelling and downwelling radiances. Because it is difficult to determine the occurrence of temperature inversion from satellite data, the influence of ATI on the retrieval of land surface temperature (LST) was not considered in the development of LST retrieval algorithm. This paper aims to analyze and reduce the influence of ATI on LST retrieval in the generalized split-window (GSW) algorithm. The GSW coefficients are reparameterized by using the ATI profiles from the thermodynamic initial guess retrieval cloud-free database. Comparison of the root-mean-square errors calculated before and after using the reparameterized coefficients in the GSW algorithm from the numerical simulations showed that the LST retrieval accuracy could be improved by 0.71 K for viewing zenith angle equivalent to 60°. Intercomparisons using the moderate resolution imaging spectroradiometer products MOD11_L2/MYD11_L2 and in situ LST measured at the Hailar field site in northeastern Inner Mongolia, China, showed that the LST retrieval accuracy could be improved by 0.4 K using the reparamerization coefficients in the GSW algorithm when the atmosphere is occurred by ATI.

Long-term assessment of the CALIPSO Imaging Infrared Radiometer (IIR) calibration and stability through simulated and observed comparisons with MODIS/Aqua and SEVIRI/Meteosat

Abstract. The quality of the calibrated radiances of the medium-resolution Imaging Infrared Radiometer (IIR) on-board the CALIPSO (Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observation) satellite is quantitatively evaluated from the beginning of the mission in June 2006. Two complementary relative and stand-alone approaches are used, which are related to comparisons of measured brightness temperatures and to model-to-observations comparisons, respectively. In both cases, IIR channels 1 (8.65 µm), 2 (10.6 µm), and 3 (12.05 µm) are paired with the Moderate Resolution Imaging Spectroradiometer (MODIS)/Aqua Collection 5 companion channels 29, 31, and 32, respectively, as well as with the Spinning Enhanced Visible and Infrared Imager (SEVIRI)/Meteosat companion channels IR8.7, IR10.8, and IR12, respectively. These pairs were selected before launch to meet radiometric, geometric, and space-time constraints. The prelaunch studies were based on simulations and sensitivity studies using the 4A/OP radiative transfer model and the more than 2300 atmospheres of the climatological Thermodynamic Initial Guess Retrieval (TIGR) input dataset further sorted into five air mass types. Using data from over 9.5 years of on-orbit operation, and following the relative approach technique, collocated measurements of IIR and of its companion channels have been compared at all latitudes over ocean, during day and night, and for all types of scenes in a wide range of brightness temperatures. The relative approach shows an excellent stability of IIR2–MODIS31 and IIR3–MODIS32 brightness temperature differences (BTDs) since launch. A slight trend within the IIR1–MODIS29 BTD, that equals −0.02 K yr−1 on average over 9.5 years, is detected when using the relative approach at all latitudes and all scene temperatures. For very cold scene temperatures (190–200 K) in the tropics, each IIR channel is warmer than its MODIS companion channel by 1.6 K on average. For the stand-alone approach, clear sky measurements only are considered, which are directly compared with simulations using 4A/OP and collocated ERA-Interim (ERA-I) reanalyses. The clear sky mask is derived from collocated observations from IIR and the CALIPSO lidar. Simulations for clear sky pixels in the tropics reproduce the differences between IIR1 and MODIS29 within 0.02 K and between IIR2 and MODIS31 within 0.04 K, whereas IIR3–MODIS32 is larger than simulated by 0.26 K. The stand-alone approach indicates that the trend identified from the relative approach originates from MODIS29, whereas no trend (less than ±0.004 K yr−1) is identified for any of the IIR channels. Finally, using the relative approach, a year-by-year seasonal bias between nighttime and daytime IIR–MODIS BTD was found at mid-latitude in the Northern Hemisphere. It is due to a nighttime IIR bias as determined by the stand-alone approach, which originates from a calibration drift during day-to-night transitions. The largest bias is in June and July when IIR2 and IIR3 are warmer by 0.4 K on average, and IIR1 is warmer by 0.2 K.

Infrared Continental Surface Emissivity Spectra Retrieved from AIRS Hyperspectral Sensor

Abstract Atmospheric Infrared Sounder (AIRS; NASA Aqua platform) observations over land are interpreted in terms of monthly mean surface emissivity spectra at a resolution of 0.05 μm and skin temperature. For each AIRS observation, an estimation of the atmospheric temperature and water vapor profiles is first obtained through a proximity recognition within the thermodynamic initial guess retrieval (TIGR) climatological library of about 2300 representative clear-sky atmospheric situations. With this a priori information, all terms of the radiative transfer equation are calculated by using the Automatized Atmospheric Absorption Atlas (4A) line-by-line radiative transfer model. Then, surface temperature is evaluated by using a single AIRS channel (centered at 12.183 μm) chosen for its almost constant emissivity with respect to soil type. Emissivity is then calculated for a set of 40 atmospheric windows (transmittance greater than 0.5) distributed over the AIRS spectrum. The overall infrared emissivity spectrum at 0.05-μm resolution is finally derived from a combination of high-spectral-resolution laboratory measurements of various materials carefully selected within the Moderate-Resolution Imaging Spectroradiometer/University of California, Santa Barbara (MODIS/UCSB) and Advanced Spaceborne Thermal Emission and Reflection Radiometer/Jet Propulsion Laboratory (ASTER/JPL) emissivity libraries. It is shown from simulations that the accuracy of the method developed in this paper, the multispectral method (MSM), varies from about 3% around 4 μm to considerably less than 1% in the 10–12-μm spectral window. Three years of AIRS observations (from April 2003 to March 2006) between 30°S and 30°N have been processed and interpreted in terms of monthly mean surface skin temperature and emissivity spectra from 3.7 to 14.0 μm at a spatial resolution of 1° × 1°. AIRS retrievals are compared with the MODIS (also flying aboard the NASA/Aqua platform) monthly mean L3 products and with the University of Wisconsin Cooperative Institute for Meteorological Satellite Studies baseline-fit method (UW/CIMSS BF) global infrared land surface emissivity database.

Total water vapor column retrieval from MSG-SEVIRI split window measurements exploiting the daily cycle of land surface temperatures

A physically based algorithm for the retrieval of total water vapor column (TWC) over cloud-free land surfaces proposed by Kleespies and McMillin [Kleespies, J.T., McMillin L.M. (1990). Retrieval of precipitable water from observations in the Split Window over varying surface temperatures. Journal of Applied Meteorology, 29, 851–862.] is evaluated and extended for use in atmospheric correction and surface irradiance calculation schemes. Thermal infrared split window channels at 10.8 and 12.0 μm of the MSG-SEVIRI (Meteosat Second Generation-Spinning Enhanced Visible and Infrared Imager) instrument are used. The proposed algorithm takes advantage of the improved measurement capabilities of the MSG-SEVIRI instrument with its 15 min temporal resolution and its radiometric accuracy of 0.25 K and 0.37 K in the 10.8 and 12.0 μm channels. The temporal resolution allows exploitation of the daily land surface temperature variation. There is no further need for explicit auxiliary information on air and land surface temperatures, which is difficult to obtain on an operational basis. Updated coefficients for the split window parameterization are derived based on simulations of ‘top-of-atmosphere’ SEVIRI brightness temperatures for the globally representative Thermodynamic Initial Guess Retrieval (TIGR3) set of radiosonde profiles. It turns out that the linear dependency on the transmission ratio in both split window channels as originally proposed by Kleespies & McMillin [Kleespies, J.T., McMillin L.M. (1990). Retrieval of precipitable water from observations in the Split Window over varying surface temperatures. Journal of Applied Meteorology, 29, 851–862.] has to be extended towards a non-linear approach in order to make it applicable to the full range of global atmospheric conditions. Sensitivity studies reveal that the parameterization relies on the availability of input brightness temperatures with a variation larger than approximately 5 K during the daily cycle. The new TWC algorithm was tested with MSG-SEVIRI data for European and African regions for the period March–August 2004 and compared with radiosonde data. The results show that the algorithm is capable of producing TWC values with a mean bias of − 0.2 mm and an RMSE of 6.8 mm. From the total amount of 2583 coincidences for all viewing zenith angles both for winter and summer conditions, 82% were within a ± 5 mm and 94% were within a ± 10 mm difference interval between MSG-based and radiosonde-based TWC. A second comparison to European GPS measurements for the same period from March to August 2004 reveals a bias of − 3.0 mm and an RMSE of 6.0 mm. This result is obtained for 11 UTC GPS measurements which proved to match best the MSG-TWC values. Comparing MSG-TWC to daily cloud-free mean GPS values shows a lower bias of − 2.56 mm and an increased RMSE of 6.6 mm. These findings support the usefulness of the new MSG-based algorithm for surface irradiance calculations and atmospheric correction purposes.

AIRS channel selection for CO2 and other trace‐gas retrievals

AbstractNew high‐resolution infrared sounders, such as the National Aeronautics and Space Administration's Aqua satellite Atmospheric Infrared Sounder (AIRS) or the European Meteorological Satellite systems, MetOp Infrared Atmospheric Sounding Instrument (IASI), are expected to improve our capability to monitor atmospheric carbon dioxide (CO2) and other trace‐gas concentrations from space. As they present thousands of channels, the first problem arising is the selection of a set of channels presenting the best properties to retrieve these concentrations. A new method, the Optimal Sensitivity Profile (OSP) method, based on the study of the sensitivities of AIRS channels to variations in the vertical of the different atmospheric components, is proposed. It is then compared with two ‘classical’ methods based on the information content and the degrees of freedom for signal of the AIRS channels regarding CO2. Applying the OSP method to a set of 82 representative atmospheric situations obtained from the 2311 situations of the Thermodynamic Initial Guess Retrieval (TIGR) dataset, divided into three air masses (tropical, temperate and polar), and using simulated AIRS observations, a global set of 43 channels, well covering the whole atmospheric column, is selected for CO2 retrieval. The OSP method is finally used to select channels for retrieval of atmospheric nitrous oxide (N2O), carbon monoxide (CO) and methane (CH4) concentrations. Copyright © 2003 Royal Meteorological Society

TIGR‐like atmospheric‐profile databases for accurate radiative‐flux computation

AbstractThis note summarizes the characteristics of different topological methods for sampling vertical profiles of heterogeneous variables, like atmospheric temperature and water‐vapour concentration. The methods presented follow the approach developed for the successive Thermodynamic Initial Guess Retrieval (TIGR) databases at Laboratoire de Météorologie Dynamique. The most recent one is applied to select limited numbers of profiles (some thousands) by the sampling of a much larger dataset (more than one million profiles) from the European Centre for Medium‐Range Weather Forecasts short‐range forecasts. The sampled datasets are then used for (raining a neural‐net work‐based radiative‐flux profile computation model (NeuroFlux).

A Neural Network Approach for a Fast and Accurate Computation of a Longwave Radiative Budget

Abstract The authors have investigated the possibility of elaborating a new generation of radiative transfer models for climate studies based on the neural network technique. The authors show that their neural network–based model, NeuroFlux, can be used successfully for accurately deriving the longwave radiative budget from the top of the atmosphere to the surface. The reliable sampling of the earth’s atmospheric situations in the new version of the TIGR (Thermodynamic Initial Guess Retrieval) dataset, developed at the Laboratoire de Meteorologie Dynamique, allows for an efficient learning of the neural networks. Two radiative transfer models are applied to the computation of the radiative part of the dataset: a line-by-line model and a band model. These results have been used to infer the parameters of two neural network–based radiative transfer codes. Both of them achieve an accuracy comparable to, if not better than, the current general circulation model radiative transfer codes, and they are much fast...