TIROS Operational Vertical Sounder Research Articles

Synergistic approach of frozen hydrometeor retrievals: considerations on radiative transfer and model uncertainties in a simulated framework

Abstract. In cloudy situations, infrared (IR) and microwave (MW) observations are complementary, with infrared observations being sensitive to small cloud droplets and ice particles and with microwave observations being sensitive to precipitation. This complementarity can lead to fruitful synergies in precipitation science (e.g., Kidd and Levizzani, 2022). However, several sources of errors do exist in the treatment of infrared and microwave data that could prevent such synergy. This paper studies several of these sources to estimate their impact on retrievals. To do so, simulations from the radiative transfer (RT) for TIROS Operational Vertical Sounder (RTTOV v13) are used to build simulated observations. Indeed, we make use of a fully simulated framework to explain the impacts of the identified errors. A combination of infrared and microwave frequencies is built within a Bayesian inversion framework. Synergy is studied using different experiments: (i) with several sources of errors eliminated, (ii) with only one source of errors considered at a time and (iii) with all sources of errors together. The derived retrievals of frozen hydrometeors for each experiment are examined in a statistical study of 15 d in summer and 15 d in winter over the Atlantic Ocean. One of the main outcomes of the study is that the combination of infrared and microwave frequencies takes advantage of the strengths of both spectral ranges, leading to more accurate retrievals. Each source of error has more or less impact depending on the type of hydrometeor. Another outcome of the study is that, in all cases explored, even though the radiative transfer and numerical modeling errors may decrease the magnitude of benefits generated by the combination of infrared and microwave frequencies, the compromise remains positive.

Using CloudSat to Advance the Global Precipitation Climatology Project (GPCP) over Antarctica

Remote sensing-based precipitation products face several challenges in high latitudes and specifically over frozen surfaces (i.e., snow and ice). Consequently, precipitation estimates tend to be lower in quality over these regions, including Antarctica, the coldest continent on Earth. In this study, we developed a method for adjusting precipitation estimates over Antarctica by leveraging CloudSat's ability to capture snowfall compared to other satellite products over snow and ice surfaces. We addressed limitations of CloudSat, such as poor spatiotemporal sampling, noise, and incomplete coverage near the poles. We utilized the European Centre for Medium-Range Weather Forecasts (ECMWF) Reanalysis 5th Generation (ERA5) to guide development, particularly during the period when the TIROS Operational Vertical Sounder (TOVS) was contributing. We adjusted monthly and seasonal TOVS and Atmospheric Infrared Sounder – Infrared (AIRS-IR) precipitation biases over Antarctica at the pixel level, as these two products are the primary inputs for the Global Precipitation Climatology Project (GPCP) in high latitudes. We assessed the adjusted TOVS and AIRS-IR through analyses of geographical maps and time series of monthly and seasonal mean precipitation rates. The results are encouraging, indicating that the proposed approach could replace the current approach used in the GPCP for adjusting precipitation estimates from AIRS-IR and TOVS over Antarctica. Adjusting precipitation estimates from TOVS and AIRS-IR using the proposed approach improves the Kling-Gupta efficiency (KGE) over their entire period by 162% and 147%, respectively. Moreover, the proposed approach can be applied to adjust other precipitation products over Antarctica at the pixel level, including satellites and reanalysis products.

Global evaluation of fast radiative transfer model coefficients for early meteorological satellite sensors

Abstract. RTTOV (the Radiative Transfer for TOVS code, where TOVS is the TIROS Operational Vertical Sounder) coefficients are evaluated using a large, independent dataset of 25 000 atmospheric model profiles as a robust test of the diverse 83 training profiles typically used. The study is carried out for nine historical satellite instruments: the InfraRed Interferometer Spectrometer D (IRIS-D), Satellite Infrared Spectrometer B (SIRS-B), Medium Resolution Infrared Radiometer (MRIR) and High Resolution Infrared Radiometer (HRIR) for the infrared part of the spectrum, and the Microwave Sounding Unit (MSU), Special Sensor Microwave Imager (SSM/I), Special Sensor Microwave – Humidity (SSM/T-2), Scanning Multichannel Microwave Radiometer (SMMR) and Special Sensor Microwave Imager/Sounder (SSMI/S) for the microwave. Simulated channel brightness temperatures show similar statistics for both the independent and the 83-profile datasets, confirming that it is acceptable to validate the RTTOV coefficients with the same profiles used to generate the coefficients. Differences between the RTTOV and the line-by-line models are highest in water vapour channels, where mean values can reach up to 0.4 ± 0.2 K for the infrared and 0.04 ± 0.13 K for the microwave. Examination of the latitudinal dependence of the bias reveals different patterns of variability for similar channels on different instruments, such as the channel centred at 679 cm−1 on both IRIS-D and SIRS-B, showing the importance of the specification of the instrumental spectral response functions (ISRFs). Maximum differences of up to several kelvin are associated with extremely non-typical profiles, such as those in polar or very hot regions.

Impact of a New Bias Correction Predictor for FY‐4A AGRI All‐Sky Data Assimilation on Typhoon Forecast

AbstractThe all‐sky assimilation module for Advanced Geostationary Radiation Imager (AGRI) onboard the Chinese new generation of geostationary meteorological satellites Fengyun‐4A (FY‐4A) is constructed for the first time in Weather Research and Forecasting Model Data Assimilation (WRFDA) model with Radiative Transfer for the TIROS Operational Vertical Sounder (RTTOV). Based on the characteristics of bias distribution, the cloud effect average is selected as a new bias correction (BC) predictor to remove cloud‐related biases in all‐sky conditions. The impact of the modified BC scheme and AGRI all‐sky assimilation on typhoon Lekima (2019) forecast is evaluated through a set of cycle assimilation experiments with WRFDA 3DVAR component. The result shows that, compared with other BC schemes, the modified BC scheme can effectively reduce the mean standard deviation, root mean square error, and absolute mean of observation departure in all‐sky conditions. Meanwhile, the AGRI all‐sky assimilation with the modified BC scheme obtains significant improvements in track prediction. Substantial error reductions in model variables, such as wind, temperature, and humidity, are also produced.

Simulation of a clear sky satellite image in water vapor and infrared satellite M.S.G channel’s

Radiative transfer models are an important tool for the scientific community. Such models simulate the radiative transfer processes of the atmosphere at a given wavelength or spectral region for a given set of surface and atmospheric conditions. It is used to simulate the radiances and brightness temperatures of various satellite sensors. RTTOV stands for ‘Radiative Transfer for TIROS Operational Vertical Sounder’ and refers to the computational efficient algorithms that have been developed by Eumetsat within the framework of SAF to meet the requirements of the operational data assimilation system. The aim of our work is to allow a simulation of infrared image of MSG2 satellite, by the RTTOV model, starting from the temperature and humidity profiles resulting from a weather forecasting model. The results are satisfactory, the evaluation have shown that the RTTOV model is effective enough for water vapor channels, while it appears less effective for the rest of the channels.

Direct Assimilation of Ground-Based Microwave Radiometer Clear-Sky Radiance Data and Its Impact on the Forecast of Heavy Rainfall

Ground-based microwave radiometer (GMWR) data with high spatial and temporal resolution can improve the accuracy of weather forecasts when effectively assimilated into numerical weather prediction. Nowadays, the major method to assimilate these data is via indirect assimilation by assimilating the retrieved profiles, which introduces large retrieval errors and cannot easily be represented by an error covariance matrix. Direct assimilation, on the other hand, can avoid this issue. In this study, the ground-based version of the Radiative Transfer for the TIROS Operational Vertical Sounder (RTTOV-gb) was selected as the observation operator, and a direct assimilation module for GMWR radiance data was established in the Weather Research and Forecasting Model Data Assimilation (WRFDA). Then, this direct assimilation module was applied to assimilate GMWR data. The results were compared to the indirect assimilation experiment and demonstrated that direct assimilation can more effectively improve the model’s initial fields in terms of temperature and humidity than indirect assimilation while avoiding the influence of retrieval errors. In addition, direct assimilation performed better in the precipitation forecast than indirect assimilation, making the main precipitation center closer to the observation. In particular, the improvement in the precipitation forecast with a threshold of 60 mm/6 h was obvious, and the corresponding TS score was significantly enhanced.

Comparative Study of the Atmospheric Gas Composition Detection Capabilities of FY-3D/HIRAS-I and FY-3E/HIRAS-II Based on Information Capacity

Fengyun-3E (FY-3E)/Hyperspectral Infrared Atmospheric Sounder-II (HIRAS-II) is an extension Fengyun-3D (FY-3D)/HIRAS-I. It is crucial to fully explore and analyze the detection capabilities of these two instruments for atmospheric gas composition. Based on the observed spectral data from the infrared hyperspectral detection instruments FY-3D/HIRAS-I and FY-3E/HIRAS-II, simulated radiance data and Jacobian matrices are obtained using the Rapid Radiative Transfer Model RTTOV (Radiative Transfer for TOVS (TIROS Operational Vertical Sounder)). By perturbing temperature (T), surface temperature (Tsurf), water vapor (H2O), ozone (O3), carbon dioxide (CO2), methane (CH4), carbon monoxide (CO), and nitrous oxide (N2O), the brightness temperature differences before and after the perturbations are calculated to analyze the sensitivity of temperature and various atmospheric gas components. The Improved Optimal Sensitivity Profile (OSP) algorithm is used to select the channels for atmospheric gas retrieval. The observation error covariance and background error covariance matrices are calculated, and then the information capacity is calculated, specifically the degrees of freedom for signal(DFS) and the entropy reduction (ER). Based on this, a comparative analysis is conducted on the information capacity of atmospheric water vapor and ozone components contained in the hyperspectral detection data from HIRAS-I and HIRAS-II instruments, respectively, to explore the retrieval capabilities of the two instruments for atmospheric gas components. We selected clear-sky data from the African oceanic region and the Chinese Yangtze River Delta terrestrial region for quantitative analysis of the information capacity of HIRAS-I and HIRAS-II. The results show that FY-3D/HIRAS-I and FY-3E/HIRAS-II exhibit different sensitivities to atmospheric gas components. In different experimental regions, temperature and water vapor show the most dramatic sensitivity changes, followed by ozone, methane, and nitrous oxide, while carbon monoxide and carbon dioxide exhibit the lowest variability. Regarding channel selection, HIRAS-II identifies more gas channels compared to HIRAS-I. The experiments concluded that HIRAS-II has a significantly higher information capacity than HIRAS-I, and the information capacity of atmospheric gas components varies across different experimental regions. Water vapor and ozone exhibit the highest information capacity, followed by nitrous oxide and methane, while carbon monoxide and carbon dioxide demonstrate the lowest capacity. The H2O ER (DFS) contained in FY-3E/HIRAS-II is 1.51 (0.35) higher than that in FY-3D/HIRAS-I, the O3 ER (DFS) in FY-3E/HIRAS-II is 1.51 (0.36) higher than that in FY-3D/HIRAS-I, while the N2O ER (DFS) in FY-3E/HIRAS-II is 0.17 (0.19) higher and the CH4 ER (DFS) is 0.07 (0.04) higher than that in FY-3D/HIRAS-I.

Effects of joint assimilation of FY-4A AGRI and ground-based microwave radiometer on heavy rainfall prediction

As the latest generation of Chinese Geostationary Weather Satellites, Fengyun-4 carries the Advanced Geosynchronous Radiation Imager (AGRI), which has more spectral bands and higher temporal and spatial resolution than the Visible Infrared Spin-Scan Radiometer (VISSR) onboard geostationary satellite FY-2. Direct assimilation of the FY-4A AGRI datasets has been proved to be an efficient way to improve heavy rainfall simulation. We aim to assess the joint assimilation of AGRI infrared radiance and ground-based MWR (Microwave Radiometer) data on short-duration heavy rainfall prediction. RTTOV (Radiative Transfer for the TIROS Operational Vertical Sounder) is used as the observational operator for FY-4A AGRI data assimilation. The data assimilation interface is built in WRFDA 4.3 to achieve direct assimilation of FY4A AGRI radiance. The forecasting effectiveness of the joint assimilation for a typical heavy rainfall event over northern China is analyzed with four simulation experiments. The main conclusions are: 1) Assimilating MWR data can improve the initial humidity condition in the middle-lower layers, while AGRI radiance assimilation favors the initial humidity correction in the middle-upper layers. The joint assimilation of two datasets can remarkably improve the initial humidity condition in the entire column. 2) Data assimilation effectively improves the 6-h accumulated rainfall simulation. The joint assimilation of AGRI radiance and MWR data is superior to assimilating either of them. The joint assimilation significantly improves the rainfall forecast over the Beijing area, where the seven MWRs are distributed. 3) Data assimilation experiments present similar effects on predicted and initial humidity conditions. The MWR_DA experiment (only assimilate MWR data) markedly improves the humidity forecast in the middle-lower layers, while AGRI_DA (only assimilate AGRI data) is effective in the middle-upper layers. The joint assimilation of AGRI radiance and MWR data could skillfully correct the humidity distribution in the entire layers, allowing for more accurate heavy rainfall prediction. This paper provides a valuable basis for further improving the application of FY-4A AGRI radiance in numerical weather models.

Evaluation and Global-Scale Observation of Nitrous Oxide from IASI on Metop-A

Nitrous oxide (N2O) is a greenhouse gas difficult to estimate by satellite because of its weak spectral signature in the infra-red band and its low variability in the troposphere. Nevertheless, this study presents the evaluation of new tropospheric N2O observations from the Infrared Atmospheric Sounder Interferometer (IASI) on Metop-A using the Toulouse N2O Retrieval Version 2.0 tool. This tool is based on the Radiative Transfer for Tiros Operational Vertical sounder (RTTOV) model version 12.3 coupled to the Levenberg-Marquardt optimal estimation method enabling the simultaneous retrieval of methane, water vapour, temperature profiles together with surface temperature and emissivity within the 1240–1350 cm−1 window. In this study, we focused on the upper troposphere (300 hPa) where the sensitivity of IASI is significant. The IASI N2O data has been evaluated using aircraft N2O observations from the High-performance Instrumented Airborne Platform for Environmental Research Pole-to-Pole Observations (HIPPO) campaigns in 2009, 2010, and 2011 and from the National Oceanic and Atmospheric Administration’s (NOAA) Global Greenhouse Gas Reference Network (GGGRN) in 2011. In addition, we evaluated the IASI N2O using ground-based N2O measurements from 9 stations belonging to the Network for the Detection of Atmospheric Composition Change (NDACC). We found a total random error of ∼2 ppbv (0.6%) for one single retrieval at 300 hPa. Under favorable conditions, this error is also found in the vertical level pressure range 300–500 hPa. It decreases rapidly to ∼0.4 ppbv (0.1%) when we average on a 1° × 1° box. In addition, independent observations allows the estimation of bias with the IASI TN2OR v2.0 N2O. The bias between IASI and aircraft N2O data at 300 hPa is ∼1.0 ppbv (∼0.3%). We found an estimated random error of ∼2.3 ppbv (∼0.75%). This study also shows relatively high correlations between IASI data and aircraft in situ profiles but more varying correlations over the year 2011 depending on the location between IASI and NDACC remote sensing data. Finally, we present daily, monthly, and seasonal IASI N2O horizontal distributions in the upper troposphere as well as cross sections for different seasons that exhibit maxima in the Tropical band especially over Africa and South America.

Optimal Estimation MSG-SEVIRI Clear-Sky Total Column Water Vapour Retrieval Using the Split Window Difference

A new algorithm for the retrieval of day-time total column water vapour (TCWV) from measurements of a MSG-SEVIRI (Meteosat Second Generation Spinning Enhanced Visible and Infrared Imager) instrument is presented. The retrieval is based on a forward operator, at the core of which lies Radiative Transfer for TIROS Operational Vertical Sounder (RTTOV). This forward model relates TCWV and surface temperature to brightness temperatures in the split window at 11 and 12µm with the use of a first guess for temperature and humidity profiles from the ERA5 reanalysis. The forward model is then embedded in a full Optimal Estimation (OE) method, which yields pixel by pixel uncertainty estimates and performance indicators. The algorithm is applicable to any instrument which features the split window configuration, given a first guess for atmospheric conditions (i.e., from NWP) and an estimate of surface emissivity at 11 µm. The algorithm was developed within the framework of RealPEP (Near-Realtime Quantitative Precipitation Estimation and Prediction) in which the advancement of the estimation and nowcasting of extreme precipitation and flooding in Germany are studied. Thus, processing and validation has been limited to the German domain. Three independent ground-based TCWV observation data sets were used as reference, i.e., AERONET (Aerosol Robotic Network), GNSS Germany (Global Navigation Satellite System) and measurements from two MWR (Microwave Radiometer) sites. The validation concludes with good agreement, with absolute biases between 0.11 and 2.85 kg/m2, root mean square deviations (rmsds) between 1.63 and 3.24 kg/m2 and Pearson correlation coefficients ranging from 0.96 to 0.98. The retrievals uncertainty estimates were evaluated against AERONET. The comparison suggests that, in sum, uncertainties are estimated well, while still some error sources seem to be over- and underestimated. In limited case studies it could be shown that SEVIRI TCWV is capable to both display large scale variabilities in water vapour fields and reproduce the daily course of water vapour exposed by ground-based observations.

Diurnal Variability of Surface Temperature over Lakes: Case Study for Lake Huron

The significance of the diurnal variability of Lake Surface Temperature (LST) has been recognized; yet, its magnitude in terms of spatial and temporal variability is not well known. Attempts have been made to derive such information from satellites at a high spatial resolution; however, most have been made from polar orbiting satellites that sample only twice per day. We have developed an approach to derive such information from geostationary satellites at an hourly time scale and at a spatial resolution of about 5 km. The approach to derive LST uses the Radiative Transfer for TIROS Operational Vertical Sounder (TOVS) (RTTOV) model driven by the Modern-Era Retrospective analysis for Research and Applications (MERRA)-2 information. The methodology has been implemented over Lake Huron for about six years. We present the results of the evaluation against various independent satellite products and demonstrate that there is a strong diurnal variability in the skin temperature over the lake and that the lowest and highest values, as derived twice per day from polar orbiting satellites, may not represent the magnitude of the Diurnal Temperature Range (DTR).

Discrete Ordinate Adding Method (DOAM), a new solver for Advanced Radiative transfer Modeling System (ARMS).

Satellite data assimilation requires a computationally fast and accurate radiative transfer model. Currently, three fast models are commonly used in the Numerical Weather Prediction models (NWP) for satellite data assimilation, including Radiative Transfer for TIROS Operational Vertical Sounder (RTTOV), Community Radiative Transfer Model (CRTM), and Advanced Radiative transfer Modeling System (ARMS). ARMS was initiated in 2018 and is now becoming the third pillar supporting many users in NWP and remote sensing fields. Its radiative transfer solvers (e.g. Doubling Adding method) is inherited from CRTM. In this study, we propose a Discrete Ordinate Adding Method (DOAM) to solve the radiative transfer equation including both solar and thermal source terms. In order to accelerate the DOAM computation, the single scattering approximation is used in the layer with an optical depth less than 10-8 or a single scattering albedo less than 10-10. From principles of invariance, the adding method is then applied to link the radiances between the layers. The accuracy of DOAM is evaluated through four benchmark cases. It is shown that the difference between DOAM and DIScrete Ordinate Radiative Transfer (DISORT) decreases with an increase of stream number. The relative bias of the 4-stream DOAM ranges from -5.03 % to 5.92 % in the triple layers of a visible wavelength case, while the maximum bias of the 8-stream DOAM is only about 1 %. The biases can be significantly reduced by the single scattering correction. Comparing to the visible case, the accuracy of the 4-stream DOAM is much higher in the thermal case with a maximum bias -1.69 %. Similar results are also shown in two multiple-layer cases. In the MacBook Pro (15-inch, 2018) laptop, the 2-stream DOAM only takes 1.68 seconds for calculating azimuthally independent radiance of 3000 profiles in the hyper-spectral oxygen A-band (wavelength ranges from 0.757 µm to 0.775 µm), while the 4-stream DOAM takes 4.06 seconds and the 16-stream DOAM takes 45.93 seconds. The time of the 2-, 4- and 16- stream DOAM are 0.86 seconds, 1.09 seconds and 4.34 seconds for calculating azimuthally averaged radiance. DISORT with 16 streams takes 1521.56 seconds and 127.64 seconds under the same condition. As a new solver, DOAM has been integrated into ARMS and is used to simulate the brightness temperatures at MicroWave Humidity Sounder (MWHS) as well as MicroWave Radiation Imager (MWRI) frequencies. The simulations by DOAM are compared to those by Doubling Adding method and accuracy of both solvers shows a general agreement. All the results show that the DOAM is accurate and computational efficient for applications in NWP data assimilation and satellite remote sensing.

Numerical Simulation of Near-Surface Wind during a Severe Wind Event in a Complex Terrain by Multisource Data Assimilation and Dynamic Downscaling

Accurate forecast and simulation of near-surface wind is a great challenge for numerical weather prediction models due to the significant transient and intermittent nature of near-surface wind. Based on the analyses of the impact of assimilating in situ and Advanced Tiros Operational Vertical Sounder (ATOVS) satellite radiance data on the simulation of near-surface wind during a severe wind event, using the new generation mesoscale Weather Research and Forecasting (WRF) model and its three-dimensional variational (3DVAR) data assimilation system, the dynamic downscaling of near-surface wind is further investigated by coupling the microscale California Meteorological (CALMET) model with the WRF and its 3DVAR system. Results indicate that assimilating in situ and ATOVS radiance observations strengthens the airflow across the Alataw valley and triggers the downward transport of momentum from the upper atmosphere in the downstream area of the valley in the initial conditions, thus improving near-surface wind simulations. Further investigations indicate that the CALMET model provides more refined microtopographic structures than the WRF model in the vicinity of the wind towers. Although using the CALMET model achieves the best simulation of near-surface wind through dynamic downscaling of the output from the WRF and its 3DVAR assimilation, the simulation improvements of near-surface wind speed are mainly within 1 m s−1. Specifically, the mean improvement proportions of near-surface wind speed are 64.8% for the whole simulation period, 58.7% for the severe wind period, 68.3% for the severe wind decay period, and 75.4% for the weak wind period. The observed near-surface wind directions in the weak wind conditions are better simulated in the coupled model with CALMET downscaling than in the WRF and its 3DVAR system. It is concluded that the simulation improvements of CALMET downscaling are distinct when near-surface winds are weak, and the downscaling effects are mainly manifested in the simulation of near-surface wind directions.

All‐sky microwave humidity sounder assimilation in the Korean Integrated Model forecast system

AbstractThe Korean Integrated Model (KIM) and hybrid data assimilation system were extended to assimilate all‐sky radiance from microwave satellite sensors. Initially, a radiative transfer model for the TIROS Operational Vertical Sounder (TOVS), called RTTOV‐SCATT (version 11.3), was implemented to assimilate the Microwave Humidity Sounder (MHS) 183 GHz channels over the ocean. Cloud and precipitation parameters are not directly assimilated into our system, but temperature and humidity profiles are improved in the all‐sky assimilation. In the cycled analysis and forecast experiments, an assimilation of the MHS in a cloudy region shows globally substantial benefits: the coverage of the MHS radiance data is increased by 23–28% in the all‐sky assimilation. It is demonstrated that RTTOV‐SCATT well describes the brightness temperature (TB) on selected cloudy pixels over the Tropics and the Southern Hemisphere, with potential for linking the TB innovation to the increment for the hydrometeor variable. As a result, a reduction of 1.11% in the specific humidity root‐mean‐square error occurs in the 6‐hr forecast field as compared to the European Centre for Medium‐Range Weather Forecasts (ECMWF)'s Integrated Forecasting System (IFS) analysis. Even though the all‐sky MHS assimilation's impact on the Northern Hemisphere midlatitudes is not remarkable, it nonetheless produces an improved humidity analysis increment for a heavy rainfall case over East Asia.

The development of the Atmospheric Measurements by Ultra-Light Spectrometer (AMULSE) greenhouse gas profiling system and application for satellite retrieval validation

Abstract. We report in this paper the development of an embedded ultralight spectrometer (<3 kg) based on tuneable diode laser absorption spectroscopy (with a sampling rate of 24 Hz) in the mid-infrared spectral region. This instrument is dedicated to in situ measurements of the vertical profile concentrations of three main greenhouse gases – carbon dioxide (CO2), methane (CH4) and water vapour (H2O) – via standard weather and tethered balloons. The plug and play instrument is compact, robust, cost-effective, and autonomous. The instrument also has low power consumption and is non-intrusive. It was first calibrated during an in situ experiment on an ICOS (Integrated Carbon Observation System) site for several days, then used in two experiments with several balloon flights of up to 30 km altitude in the Reims region of France in 2017–2018 in collaboration with Météo-France CNRM (Centre National de Recherches Météorologiques). This paper shows the valuable interest of the data measured by the AMULSE (Atmospheric Measurements by Ultra-Light Spectrometer) instrument during the APOGEE (Atmospheric Profiles of Greenhouse Gases) measurement experiment, specifically for the vertical profiles of CO2 and CH4, measurements of which remain very sparse. We have carried out several experiments showing that the measured profiles have several applications: the validation of simulations of infrared satellite observations, evaluating the quality of chemical profiles from chemistry transport models (CTMs) and evaluating the quality of retrieved chemical profiles from the assimilation of infrared satellite observations. The results show that the simulations of infrared satellite observations from IASI (Infrared Atmospheric Sounding Interferometer) and CrIS (Cross-track Infrared Sounder) instruments performed in operational mode for numerical weather prediction (NWP) by the radiative transfer model (RTM) RTTOV (Radiative Transfer for the TIROS Operational Vertical Sounder) are of good quality. We also show that the MOCAGE (Modèle de Chimie Atmosphérique de Grande Echelle) and CAMS (Copernicus Atmospheric Monitoring Service) CTMs modelled ozone profiles fairly accurately and that the CAMS CTM represents the methane in the troposphere well compared to MOCAGE. Finally, the measured in situ ozone profiles allowed us to show the good quality of the retrieved ozone profiles by assimilating ozone-sensitive infrared spectral radiances from the IASI and CrIS.

Performance of Radiative Transfer Models in the Microwave Region

AbstractWe compared two fast radiative transfer models, Community Radiative Transfer Model (CRTM) and Radiative Transfer for TIROS Operational Vertical Sounder (RTTOV), with the LBL model Atmospheric Radiative Transfer Simulator (ARTS). We used the measurements from Advanced Technology Microwave Sounder (ATMS) and the Global Precipitation Measurement Microwave Imager (GMI) for evaluation of the radiative transfer models. The models in comparison with the observations and each other performed very well with a mean difference less than 0.5 K for the temperature sounding channels operating near the oxygen absorption band at 60 GHz. There was a difference of up to 1 K among the models as well as compared with the observations for humidity sounding channels operating around water vapor absorption line at 183 GHz. The mean difference between the simulations and observations was up to 6 K for surface sensitive channels. Water vapor and surface sensitive channels also showed to be more sensitive than the temperature sounding channels to the spectroscopy models used to calculate the absorption coefficients. There was a small difference, less than 0.1 K, between brightness temperatures calculated using traditional boxcar and actual Sensor or Spectral Response Functions, except for a difference of 0.25 K for ATMS Channel 6. Double difference technique showed about 1 K difference between water vapor channels from ATMS instruments onboard N20 and National Polar‐orbiting Partnership (NPP). However, comparison of a new version of ATMS/NPP observations recently generated using an enhanced calibration algorithm with ATMS/N20 observations showed that the differences between the two instruments are less than 0.5 K after improving the ATMS/NPP calibration.

Temperature and Humidity Profiles Retrieval in a Plain Area from Fengyun-3D/HIRAS Sensor Using a 1D-VAR Assimilation Scheme

In this study, a one-dimensional variational (1D-VAR) retrieval system is proposed to simultaneously retrieve temperature and humidity atmospheric profiles under clear-sky conditions. Our technique requires observations from the Fengyun-3D Hyperspectral Infrared Radiation Atmospheric Sounding (HIRAS) satellite combined with the Weather Research and Forecast (WRF) model. In the method, the radiative transfer for the TIROS Operational Vertical Sounder (TOVS (RTTOV) model is also used as a forward observation operator. The accuracy of our approach was evaluated using as a case study the region of Beijing in China. Predicted temperature and humidity profiles were compared against ERA-Interim data, which was used as reference. Mean bias (MB) of the temperature profiles varied between −0.8 K to 0.9 K, while the root-mean-square error (RMSE) ranged from 0.5 K to 2.6 K. In the boundary layer, the 1D-VAR algorithm performed better compared with the first guess. In the middle troposphere, the retrievals were more dependent on the first guess. With respect to relative humidity predictions, the accuracy of the evaluation of the whole troposphere was improved with the inclusion of the satellite observations, reporting an MB varying from −5.68% to 2.83%. Compared with Atmospheric Infrared Sounder’s (AIRS’) products, our predicted temperature profiles showed a very good consistency and the humidity predictions were also of an acceptable prediction accuracy. All in all, results clearly evidenced the promising potential of our proposed approach for retrieving temperature and humidity profiles under clear-sky conditions.

Modeling of Nonlocal Thermodynamic Equilibrium Effects in the Classical and Principal Component‐Based Version of the RTTOV Fast Radiative Transfer Model

AbstractThe direct assimilation in 4D‐Var of principal component (PC) scores derived from Infrared Atmospheric Sounding Interferometer (IASI) spectra has recently been demonstrated. To maximize the exploitation of the IASI instrument, a future step is to consider the extension of the PC approach to the extraction of information from the 4.3‐μm CO2‐absorbing region. Shortwave IASI channels are currently underused compared to similar longwave channels because of day‐night variations in data usability due to departures from local thermodynamic equilibrium (LTE). In this paper, we document the introduction of non‐LTE (NLTE) effects in the PC‐based version of the radiative transfer for TIROS operational vertical sounder (RTTOV) fast radiative transfer model (PC‐RTTOV). The inclusion of NLTE effects in PC‐RTTOV has required the development of a parameterized scheme that allows the fast computation of a NLTE correction to LTE radiances. The fast NLTE model is general enough to be applied to any sensor and can be utilized to add a fast and accurate NLTE correction to polychromatic LTE spectra computed by any general radiative transfer model, including RTTOV, which now incorporates the fast NLTE model developed in this study. The accuracy of the NLTE correction is such that daytime and nighttime radiances can be simulated to almost the same degree of accuracy. The comparison with IASI observations shows that the fast NLTE model presented here performs significantly better than the fast NLTE model incorporated in the previous version of RTTOV and also that improvements have to be made to the simulation of NLTE effects at winter high latitudes.

The VIS/NIR Land and Snow BRDF Atlas for RTTOV: Comparison between MODIS MCD43C1 C5 and C6

A monthly mean land and snow Bidirectional Reflectance Distribution Function (BRDF) atlas for visible and near infrared parts of the spectrum has been developed for Radiative Transfer for Television Infrared Observation Satellite (TIROS) Operational Vertical sounder (TOVS) (RTTOV). The atlas follows the methodology of the RTTOV University of Wisconsin infrared land surface emissivity (UWIREMIS) atlas, i.e., it combines satellite retrievals and a principal component analysis on a dataset of hyper-spectral surface hemispherical reflectance or albedo. The current version of the BRDF atlas is based on the Collection 5 of the Moderate Resolution Imaging (MODIS) MCD43C1 Climate Modeling Grid BRDF kernel-driven model parameters product. The MCD43C1 product combines both Terra and Aqua satellites over a 16-day period of acquisition and is provided globally at 0.05° of spatial resolution. We have improved the RTTOV land surface BRDF atlas by using the last Collection 6 of MODIS product MCD43C1. We firstly found that the MODIS C6 product improved the quality index of the BRDF model as compared with that of C5. When compared with clear-sky top of atmosphere (TOA) reflectance of Spinning Enhanced Visible and InfraRed Imagers (SEVIRI) solar channels over snow-free land surfaces, we showed that the reflectances are simulated with an absolute accuracy of 3% to 5% (i.e., 0.03–0.05 in reflectance units) when either the satellite zenith angle or the solar zenith angle is below 70°, regardless of the MODIS collection. For snow-covered surfaces, we showed that the comparison with in situ snow spectral albedo is improved with C6 with an underestimation of 0.05 in the near infrared.

A Methodology to Adjust ATMS Observations for Limb Effect and Its Applications

AbstractThe Advanced Technology Microwave Sounders (ATMS), carried on the Suomi National Polar‐orbiting Partnership (SNPP) satellite, was launched on 28 October 2011. The ATMS is a follow‐on instrument to advanced microwave sounding unit (AMSU), currently flying on National Oceanic and Atmospheric Administration (NOAA) satellites. The primary new ATMS features are a reduced hardware package and improved gap coverage. One thing in common about cross‐track sounders is a scan perpendicular to the motion of the satellite, allowing a broad swath of measurements to be taken. But an undesirable feature is that the measurements vary with scan angle because of changes in the optical pathlength through the Earth's atmosphere between the Earth and the satellite. One approach to this problem is to limb adjust the measurements to a fixed view angle. The limb correction algorithm applied to ATMS is based on the heritage methodology originally applied to MSU and later to AMSU. The limb correction method is applied to each of the 96 ATMS field of view (FOV) per scan line, adjusting the off‐nadir FOV to the nadir view with fitting error generally within the instrumental noise. The limb‐adjusted brightness temperature were used in the original, legacy TIROS Operational Vertical Sounder, and Advanced TIROS Operational Vertical Sounder NOAA sounding product algorithms and more recently to derive the total precipitation water (TPW) retrieval over ocean, with a bias of 0.046 mm and a standard deviation of 3.43 mm, when compared with European Centre for Medium‐Range Weather Forecasts TPW data. The limb‐corrected brightness temperature can be used to detect the atmospheric weather features, such as the warm cores for tropical cyclones, and the imagery presents snapshots for quick weather signal diagnosis.