Water Vapor Absorption Band Research Articles

In-Orbit Spectral Response Function Correction and Its Impact on Operational Calibration for the Long-Wave Split-Window Infrared Band (12.0 μm) of FY-2G Satellite

During the early stage of the G satellite of the Fengyun-2 series (FY-2G), severe cold biases up to ~2.3 K occur in its measurements in the 12.0 μm (IR2) band, which demonstrate time- and scene-dependent characteristics. Similar cold biases in water vapor and carbon dioxide absorption bands of other satellites are considered to be caused by either ice contamination (physical method) or spectral response function (SRF) shift (empirical method). Simulations indicate that this cold bias of FY-2G indeed suffers from equivalent SRF shift as a whole towards the longer wavelength direction. To overcome it, a novel approach combining both physical and empirical methods is proposed. With the possible ice thicknesses tested before launch, the ice contamination effect is alleviated, while the shape of the SRF can be modified in a physical way. The remaining unknown factors for cold bias are removed by shifting the convolved SRF with an ice transmittance spectrum. Two parameters, i.e., the ice thickness (5 μm) and the shifted value (+0.15 μm), are estimated by inter-calibration with reference instruments, and the modification coefficient is also calculated (0.9885) for the onboard blackbody calibration. Meanwhile, the updated SRF was released online on 23 March 2016. For the period between July 2015 and December 2016, the monthly biases of the FY-2G IR2 band remain oscillating around zero, the majorities (~89%) of which are within ±1.0 K, while its mean monthly absolute bias is around 0.6 K. Nevertheless, the cold bias phenomenon of the IR2 band no longer exists. The combination method can be referred by other corrections for cold biases.

Algorithm for Recovery of Integrated Water Vapor Content in the Atmosphere over Land Surfaces Based on Satellite Spectroradiometer Data

An algorithm is proposed for making charts of the distribution of water vapor in the atmosphere based on multispectral images of the earth by the Ocean and Land Color Instrument (OLCI) on board of the European research satellite Sentinel-3. The algorithm is based on multiple regression fits of the spectral brightness coefficients at the upper boundary of the atmosphere, the geometric parameters of the satellite location (solar and viewing angles), and the total water vapor content in the atmosphere. A regression equation is derived from experimental data on the variation in the optical characteristics of the atmosphere and underlying surface, together with Monte-Carlo calculations of the radiative transfer characteristics. The equation includes the brightness coefficients in the near IR channels of the OLCI for the absorption bands of water vapor and oxygen, as well as for the transparency windows of the atmosphere. Together these make it possible to eliminate the effect of the reflection spectrum of the underlying surface and air pressure on the accuracy of the measurements. The algorithm is tested using data from a prototype OLCI, the medium resolution imaging spectrometer (MERIS). A sample chart of the distribution of water vapor in the atmosphere over Eastern Europe is constructed without using subsatellite data and digital models of the surface relief. The water vapor contents in the atmosphere determined using MERIS images and data provided by earthbound measurements with the aerosol robotic network (AERONET) are compared with a mean square deviation of 1.24 kg/m2.

Deep convective cloud characterizations from both broadband imager and hyperspectral infrared sounder measurements

AbstractDeep convective storms have contributed to airplane accidents, making them a threat to aviation safety. The most common method to identify deep convective clouds (DCCs) is using the brightness temperature difference (BTD) between the atmospheric infrared (IR) window band and the water vapor (WV) absorption band. The effectiveness of the BTD method for DCC detection is highly related to the spectral resolution and signal‐to‐noise ratio (SNR) of the WV band. In order to understand the sensitivity of BTD to spectral resolution and SNR for DCC detection, a BTD to noise ratio method using the difference between the WV and IR window radiances is developed to assess the uncertainty of DCC identification for different instruments. We examined the case of AirAsia Flight QZ8501. The brightness temperatures (Tbs) over DCCs from this case are simulated for BTD sensitivity studies by a fast forward radiative transfer model with an opaque cloud assumption for both broadband imager (e.g., Multifunction Transport Satellite imager, MTSAT‐2 imager) and hyperspectral IR sounder (e.g., Atmospheric Infrared Sounder) instruments; we also examined the relationship between the simulated Tb and the cloud top height. Results show that despite the coarser spatial resolution, BTDs measured by a hyperspectral IR sounder are much more sensitive to high cloud tops than broadband BTDs. As demonstrated in this study, a hyperspectral IR sounder can identify DCCs with better accuracy.

Retrieval of cloud ice water path using SAPHIR on board Megha-Tropiques over the tropical ocean

The SAPHIR sensor onboard Megha-Tropiques (MT) measures the earth emitted radiation at frequencies near the water vapor absorption band. SAPHIR operates in six frequencies ranging from 183±0.1 to 183±11GHz. These frequencies have been used to retrieve cloud ice water path (IWP) at a very high resolution. A method to retrieve IWP over the Indian ocean region is attempted in this study. The study is in two parts, in first part a radiative transfer based simulation is carried out to give an insight of using SAPHIR frequency channels for IWP retrieval, in the next part the real observations of SAPHIR and TRMM-TMI was used for IWP retrieval. The concurrent observations of SAPHIR brightness temperatures (Tbs) and TRMM TMI IWP were used in the development of the retrieval algorithm. An Eigen Vector analysis was done to identify weight of each channel in retrieving IWP; following this a two channel regression based algorithm was developed. The SAPHIR channels which are away from the water vapor absorption band were used to avoid possible water vapor contamination. When the retrieval is compared with independent test dataset, it gives a correlation of 0.80 and RMSE of 3.5%. SAPHIR derived IWP has been compared with other available global IWP products such as TMI, MSPPS, CloudSat and GPM-GMI qualitatively as well as quantitatively. PDF comparison of SAPHIR derived IWP found to have good agreement with CloudSat. Zonal mean comparison with recently launched GMI shows the strength of this algorithm.

Detection of water vapour absorption around 363 nm in measured atmospheric absorption spectra and its effect on DOAS evaluations

Abstract. Water vapour is known to absorb radiation from the microwave region to the blue part of the visible spectrum with decreasing efficiency. Ab initio approaches to model individual absorption lines of the gaseous water molecule predict absorption lines up to its dissociation limit at 243 nm.We present the first evidence of water vapour absorption near 363 nm from field measurements using data from multi-axis differential optical absorption spectroscopy (MAX-DOAS) and long-path (LP)-DOAS measurements. The identification of the absorptions was based on the recent POKAZATEL line list by Polyansky et al. (2017). For MAX-DOAS measurements, we observed absorption by water vapour in an absorption band around 363 nm with optical depths of up to 2 × 10−3. The retrieved column densities from 2 months of measurement data and more than 2000 individual observations at different latitudes correlate well with simultaneously measured well-established water vapour absorptions in the blue spectral range from 452 to 499 nm (R2 = 0.89), but the line intensities at around 363 nm are underestimated by a factor of 2.6 ± 0.5 by the ab initio model. At a spectral resolution of 0.5 nm, we derive a maximum cross section value of 2.7 × 10−27 cm2 molec−1 at 362.3 nm. The results were independent of the used literature absorption cross section of the O4 absorption, which overlays this water vapour absorption band. Also water vapour absorption around 376 nm was identified. Below 360 nm no water vapour absorption above 1.4 × 10−26 cm2 molec−1 was observed. The newly found absorption can have a significant impact on the spectral retrievals of absorbing trace-gas species in the spectral range around 363 nm. Its effect on the spectral analysis of O4, HONO and OClO is discussed.

Information content on hydrometeors from millimeter and sub-millimeter wavelengths

ABSTRACTThis study examines the information content on hydrometeors that could be provided by a future HYperspectral Microwave Sensor (HYMS) with frequencies ranging from 6.9 to 874 GHz (millimeter and sub-millimeter regions). Through optimal estimation theory the information content is expressed quantitatively in terms of degrees of freedom for signal (DFS). For that purpose the Atmospheric Radiative Transfer Simulator (ARTS) and its Jacobians are used with a set of 25 cloudy and precipitating profiles and their associated errors from the European Centre for Medium-range Weather Forecasting (ECMWF) global numerical weather prediction model.In agreement with previous studies it is shown that frequencies between 10 and 40 GHz are the most informative ones for liquid and rain water contents. Similarly, the absorption band at 118 GHz contains significant information on liquid precipitation. A set of new window channels (15.37-, 40.25-, 101-GHz) could provide additional information on the liquid phase. The most informative channels on cloud ice water are the window channels at 664 and 874 GHz and the water vapour absorption bands at 325 and 448 GHz. Regarding snow water contents, the channels having the largest DFS values are located in window regions (150-, 251-, 157-, 101-GHz). However it is necessary to consider 90 channels in order to represent 90% of the DFS. The added value of HYMS has been assessed against current Special Sensor Microwave Imager/Sounder (SSMI/S) onboard the Defense Meteorological Satellite Program (DMSP) and future (Microwave Imager/Ice Cloud Imager (MWI/ICI) onboard European Polar orbiting Satellite – Second Generation (EPS-SG)) microwave sensors. It appears that with a set of 276 channels the information content on hydrometeors would be significantly enhanced: the DFS increases by 1.7 against MWI/ICI and by 3 against SSMI/S. A number of tests have been performed to examine the robustness of the above results. The most informative channels on solid hydrometeors remain the same over land and over ocean surfaces. On the other hand, the database is not large enough to produce robust results over land surfaces for liquid hydrometeors. The sensitivity of the results to the microphysical properties of frozen hydrometeors has been investigated. It appears that a change in size distribution and scattering properties can move the large information content of the channels at 664 and 874 GHz from cloud ice to solid precipitation.

The new Passive microwave Neural network Precipitation Retrieval (PNPR) algorithm for the cross-track scanning ATMS radiometer: description and verification study over Europe and Africa using GPM and TRMM spaceborne radars

Abstract. The objective of this paper is to describe the development and evaluate the performance of a completely new version of the Passive microwave Neural network Precipitation Retrieval (PNPR v2), an algorithm based on a neural network approach, designed to retrieve the instantaneous surface precipitation rate using the cross-track Advanced Technology Microwave Sounder (ATMS) radiometer measurements. This algorithm, developed within the EUMETSAT H-SAF program, represents an evolution of the previous version (PNPR v1), developed for AMSU/MHS radiometers (and used and distributed operationally within H-SAF), with improvements aimed at exploiting the new precipitation-sensing capabilities of ATMS with respect to AMSU/MHS. In the design of the neural network the new ATMS channels compared to AMSU/MHS, and their combinations, including the brightness temperature differences in the water vapor absorption band, around 183 GHz, are considered. The algorithm is based on a single neural network, for all types of surface background, trained using a large database based on 94 cloud-resolving model simulations over the European and the African areas. The performance of PNPR v2 has been evaluated through an intercomparison of the instantaneous precipitation estimates with co-located estimates from the TRMM Precipitation Radar (TRMM-PR) and from the GPM Core Observatory Ku-band Precipitation Radar (GPM-KuPR). In the comparison with TRMM-PR, over the African area the statistical analysis was carried out for a 2-year (2013–2014) dataset of coincident observations over a regular grid at 0.5° × 0.5° resolution. The results have shown a good agreement between PNPR v2 and TRMM-PR for the different surface types. The correlation coefficient (CC) was equal to 0.69 over ocean and 0.71 over vegetated land (lower values were obtained over arid land and coast), and the root mean squared error (RMSE) was equal to 1.30 mm h−1 over ocean and 1.11 mm h−1 over vegetated land. The results showed a slight tendency to underestimate moderate to high precipitation, mostly over land, and overestimate moderate to light precipitation over ocean. Similar results were obtained for the comparison with GPM-KuPR over the European area (15 months, from March 2014 to May 2015 of coincident overpasses) with slightly lower CC (0.59 over vegetated land and 0.57 over ocean) and RMSE (0.82 mm h−1 over vegetated land and 0.71 mm h−1 over ocean), confirming a good agreement also between PNPR v2 and GPM-KuPR. The performance of PNPR v2 over the African area was also compared to that of PNPR v1. PNPR v2 has higher R over the different surfaces, with generally better estimation of low precipitation, mostly over ocean, thanks to improvements in the design of the neural network and also to the improved capabilities of ATMS compared to AMSU/MHS. Both versions of PNPR algorithm have shown a general consistency with the TRMM-PR.

Spaceborne Sun-Induced Vegetation Fluorescence Time Series from 2007 to 2015 Evaluated with Australian Flux Tower Measurements

A global, monthly averaged time series of Sun-induced Fluorescence (SiF), spanning January 2007 to June 2015, was derived from Metop-A Global Ozone Monitoring Experiment 2 (GOME-2) spectral measurements. Far-red SiF was retrieved using the filling-in of deep solar Fraunhofer lines and atmospheric absorption bands based on the general methodology described by Joiner et al, AMT, 2013. A Principal Component (PC) analysis of spectra over non-vegetated areas was performed to describe the effects of atmospheric absorption. Our implementation (SiF KNMI) is an independent algorithm and differs from the latest implementation of Joiner et al, AMT, 2013 (SiF NASA, v26), because we used desert reference areas for determining PCs (as opposed to cloudy ocean and some desert) and a wider fit window that covers water vapour and oxygen absorption bands (as opposed to only Fraunhofer lines). As a consequence, more PCs were needed (35 as opposed to 12). The two time series (SiF KNMI and SiF NASA, v26) correlate well (overall R of 0.78) except for tropical rain forests. Sensitivity experiments suggest the strong impact of the water vapour absorption band on retrieved SiF values. Furthermore, we evaluated the SiF time series with Gross Primary Productivity (GPP) derived from twelve flux towers in Australia. Correlations for individual towers range from 0.37 to 0.84. They are particularly high for managed biome types. In the de-seasonalized Australian SiF time series, the break of the Millennium Drought during local summer of 2010/2011 is clearly observed.

The Zugspitze radiative closure experiment for quantifying water vapor absorption over the terrestrial and solar infrared – Part 3: Quantification of the mid- and near-infrared water vapor continuum in the 2500 to 7800 cm<sup>−1</sup> spectral range under atmospheric conditions

Abstract. We present a first quantification of the near-infrared (NIR) water vapor continuum absorption from an atmospheric radiative closure experiment carried out at the Zugspitze (47.42° N, 10.98° E; 2964 m a.s.l.). Continuum quantification is achieved via radiative closure using radiometrically calibrated solar Fourier transform infrared (FTIR) absorption spectra covering the 2500 to 7800 cm−1 spectral range. The dry atmospheric conditions at the Zugspitze site (IWV 1.4 to 3.3 mm) enable continuum quantification even within water vapor absorption bands, while upper limits for continuum absorption can be provided in the centers of window regions. Throughout 75 % of the 2500 to 7800 cm−1 spectral range, the Zugspitze results agree within our estimated uncertainty with the widely used MT_CKD 2.5.2 model (Mlawer et al., 2012). In the wings of water vapor absorption bands, our measurements indicate about 2–5 times stronger continuum absorption than MT_CKD, namely in the 2800 to 3000 cm−1 and 4100 to 4200 cm−1 spectral ranges. The measurements are consistent with the laboratory measurements of Mondelain et al. (2015), which rely on cavity ring-down spectroscopy (CDRS), and the calorimetric–interferometric measurements of Bicknell et al. (2006). Compared to the recent FTIR laboratory studies of Ptashnik et al. (2012, 2013), our measurements are consistent within the estimated errors throughout most of the spectral range. However, in the wings of water vapor absorption bands our measurements indicate typically 2–3 times weaker continuum absorption under atmospheric conditions, namely in the 3200 to 3400, 4050 to 4200, and 6950 to 7050 cm−1 spectral regions.

Validation of a weather forecast model at radiance level against satellite observations allowing quantification of temperature, humidity, and cloud‐related biases

AbstractAn established radiative transfer model (RTM) is adapted for simulating all‐sky infrared radiance spectra from the Canadian Global Environmental Multiscale (GEM) model in order to validate its forecasts at the radiance level against Atmospheric InfraRed Sounder (AIRS) observations. Synthetic spectra are generated for 2 months from short‐term (3–9 h) GEM forecasts. The RTM uses a monthly climatological land surface emissivity/reflectivity atlas. An updated ice particle optical property library was introduced for cloudy radiance calculations. Forward model brightness temperature (BT) biases are assessed to be of the order of ∼1 K for both clear‐sky and overcast conditions. To quantify GEM forecast meteorological variables biases, spectral sensitivity kernels are generated and used to attribute radiance biases to surface and atmospheric temperatures, atmospheric humidity, and clouds biases. The kernel method, supplemented with retrieved profiles based on AIRS observations in collocation with a microwave sounder, achieves good closure in explaining clear‐sky radiance biases, which are attributed mostly to surface temperature and upper tropospheric water vapor biases. Cloudy‐sky radiance biases are dominated by cloud‐induced radiance biases. Prominent GEM biases are identified as: (1) too low surface temperature over land, causing about −5 K bias in the atmospheric window region; (2) too high upper tropospheric water vapor, inducing about −3 K bias in the water vapor absorption band; (3) too few high clouds in the convective regions, generating about +10 K bias in window band and about +6 K bias in the water vapor band.

Cirrus cloud optical and microphysical property retrievals from eMAS during SEAC4RS using bi-spectral reflectance measurements within the 1.88 μm water vapor absorption band.

Previous bi-spectral imager retrievals of cloud optical thickness (COT) and effective particle radius (CER) based on the Nakajima and King (1990) approach, such as those of the operational MODIS cloud optical property retrieval product (MOD06), have typically paired a non-absorbing visible or near-infrared wavelength, sensitive to COT, with an absorbing shortwave or midwave infrared wavelength sensitive to CER. However, in practice it is only necessary to select two spectral channels that exhibit a strong contrast in cloud particle absorption. Here it is shown, using eMAS observations obtained during NASA's SEAC4RS field campaign, that selecting two absorbing wavelength channels within the broader 1.88 μm water vapor absorption band, namely the 1.83 and 1.93 μm channels that have sufficient differences in ice crystal single scattering albedo, can yield COT and CER retrievals for thin to moderately thick single-layer cirrus that are reasonably consistent with other solar and IR imager-based and lidar-based retrievals. A distinct advantage of this channel selection for cirrus cloud retrievals is that the below-cloud water vapor absorption minimizes the surface contribution to measured cloudy TOA reflectance, in particular compared to the solar window channels used in heritage retrievals such as MOD06. This reduces retrieval uncertainty resulting from errors in the surface reflectance assumption, as well as reduces the frequency of retrieval failures for thin cirrus clouds.

On the effect of stratification of atmospheric optical characteristics on the sky radiance in the solar principal plane

The detailed information on the “in situ” vertical distribution of optical characteristics of atmospheric aerosol is frequently absent because of the complexity and high cost of environmental monitoring from aircraft. This paper presents estimates of the atmospheric optical characteristic stratification effects on downward scattered radiation measured by a sun photometer at the Earth’s surface for two basic viewing geometries: in the solar almucantar and in the solar principal plane. We obtained estimates on the basis of numerical experiments, key parameters of which imitate both background (areas with low anthropogenic load) and extreme atmospheric situations (urban smog, dust haze over land, and dust outflow over water surface). In the visible spectral region, outside the oxygen, ozone, water vapor, and nitrogen dioxide absorption bands, the stratification of the atmospheric optical characteristics is shown to have no significant effect on the sky radiance in solar almucantar, however becoming important in the solar principal plane. The main factor is the vertical behavior of the aerosol extinction coefficient. The radiance calculation errors can be substantially reduced by using the simplest vertical profiles and, in particular, the exponential distribution of the extinction coefficient over height.

Sensitivity of Intercalibration Uncertainty of the CLARREO Reflected Solar Spectrometer Features

The Climate Absolute Radiance and Refractivity Observatory (CLARREO) mission was recommended by the National Research Council in 2007 to conduct highly accurate and International System of Unit-traceable decadal change observations and provide an on-orbit intercalibration standard with high accuracy for relevant Earth observing sensors. The goal of reference intercalibration is to enable rigorous observations of critical climate change variables, including reflected broadband radiation, cloud properties, and changes in surface albedo, including snow and ice albedo feedback, to be made consistently among different sensors. This requires the CLARREO Reflected Solar Spectrometer (RSS) to provide highly accurate spectral reflectance measurements to establish an on-orbit reference with a radiometric accuracy requirement better than 0.3% $(\mathrm{k} =2) $ for existing sensors. In this paper, MODTRAN-simulated top-of-atmosphere spectral data and spectral measurements from the SCIAMACHY instrument on Envisat are used to determine sensitivity of intercalibration uncertainty on key design parameters of the CLARREO spectrometer: spectral range, sampling and resolution. Their impact on intercalibration uncertainty for MODIS and VIIRS imagers is estimated for various surface types (ocean, vegetation, desert, snow, deep convective clouds, clouds and all-sky) . Results indicate that for the visible to near-infrared spectral region (465–856 nm) , the RSS instrument under current design concept produces uncertainties of 0.16% for the spectral range and 0.3% for the sampling and resolution. However, for the water vapor absorption bands in the short wavelength infrared region (1242–1629 nm) , the same requirement is not met for sampling and resolution due to their high sensitivity to the influence of atmospheric water vapor.

Evaluating possible enhancement of INSAT-3D Sounder water vapour retrieval in the tropics

The present study makes an attempt to use hyperspectral infrared observations to define optimum sounding instrument on board Indian geostationary satellites. In a multichannel sounding instrument such as Indian National Satellite System (INSAT-3D) and Geostationary Operational Environmental Satellite Sounder, there are three channels in 6–8 µm water vapour (WV) absorption band. In this study, humidity profiles are retrieved by adding another eight channels within 6–8 µm WV absorption band. The retrieved humidity profiles are compared with that retrieved from the hyperspectral sounding instrument Infrared Atmospheric Sounding Interferometer (IASI). It has been found that improvement in humidity profile retrieval with additional eight WV channels is merely ~1%, above the mid-troposphere compared to the profiles retrieved with just three WV channels. However, the retrieval accuracy with that obtained from the IASI spectra has improved by 5–10% compared to existing three as well as eight additional WV channels of a multichannel sounder. This study concludes that in order to improve the humidity retrieval accuracy in the tropics, hyperspectral sounding observations are required since mere increasing of WV channels in a multichannel sounder is having only a small positive impact.

External-Cavity Quantum Cascade Laser Spectroscopy for Mid-IR Transmission Measurements of Proteins in Aqueous Solution.

In this work, we report mid-IR transmission measurements of the protein amide I band in aqueous solution at large optical paths. A tunable external-cavity quantum cascade laser (EC-QCL) operated in pulsed mode at room temperature allowed one to apply a path length of up to 38 μm, which is four times larger than that applicable with conventional FT-IR spectrometers. To minimize temperature-induced variations caused by background absorption of the ν2-vibration of water (HOH-bending) overlapping with the amide I region, a highly stable temperature control unit with relative temperature stability within 0.005 °C was developed. An advanced data processing protocol was established to overcome fluctuations in the fine structure of the emission curve that are inherent to the employed EC-QCL due to its mechanical instabilities. To allow for wavenumber accuracy, a spectral calibration method has been elaborated to reference the acquired IR spectra to the absolute positions of the water vapor absorption bands. Employing this setup, characteristic spectral features of five well-studied proteins exhibiting different secondary structures could be measured at concentrations as low as 2.5 mg mL(-1). This concentration range could previously only be accessed by IR measurements in D2O. Mathematical evaluation of the spectral overlap and comparison of second derivative spectra confirm excellent agreement of the QCL transmission measurements with protein spectra acquired by FT-IR spectroscopy. This proves the potential of the applied setup to monitor secondary structure changes of proteins in aqueous solution at extended optical path lengths, which allow experiments in flow through configuration.

Developing a spectral library of mangrove species of Indian east coast using field spectroscopy

Development of a spectral library is a prerequisite for the higher order classification of satellite data and hyperspectral image analysis to map any ecosystem with rich diversity. In this study, sampling methodology, collection of field and laboratory spectral signatures and post-processing methodologies were investigated for developing an exclusive spectral library of mangrove species using hyperspectral spectroscopic techniques. Canopy level field spectra and leaf level laboratory spectra were collected for 34 species (25 true and 9 associated mangroves) from two different mangrove ecosystems of the Indian east coast. Post-processing steps such as removal of water vapour absorption bands, correction of drifts which occur due to the thermal properties of the instrument during data collection and smoothing of spectra for its further utilisation were applied on collected spectra. The processed spectra were then compiled as spectral library.

Estimation of soil moisture content from the spectral reflectance of bare soils in the 0.4-2.5 µm domain.

This work aims to compare the performance of new methods to estimate the Soil Moisture Content (SMC) of bare soils from their spectral signatures in the reflective domain (0.4–2.5 μm) in comparison with widely used spectral indices like Normalized Soil Moisture Index (NSMI) and Water Index SOIL (WISOIL). Indeed, these reference spectral indices use wavelengths located in the water vapour absorption bands and their performance are thus very sensitive to the quality of the atmospheric compensation. To reduce these limitations, two new spectral indices are proposed which wavelengths are defined using the determination matrix tool by taking into account the atmospheric transmission: Normalized Index of Nswir domain for Smc estimatiOn from Linear correlation (NINSOL) and Normalized Index of Nswir domain for Smc estimatiOn from Non linear correlation (NINSON). These spectral indices are completed by two new methods based on the global shape of the soil spectral signatures. These methods are the Inverse Soil semi-Empirical Reflectance model (ISER), using the inversion of an existing empirical soil model simulating the soil spectral reflectance according to soil moisture content for a given soil class, and the convex envelope model, linking the area between the envelope and the spectral signature to the SMC. All these methods are compared using a reference database built with 32 soil samples and composed of 190 spectral signatures with five or six soil moisture contents. Half of the database is used for the calibration stage and the remaining to evaluate the performance of the SMC estimation methods. The results show that the four new methods lead to similar or better performance than the one obtained by the reference indices. The RMSE is ranging from 3.8% to 6.2% and the coefficient of determination R2 varies between 0.74 and 0.91 with the best performance obtained with the ISER model. In a second step, simulated spectral radiances at the sensor level are used to analyse the sensitivity of these methods to the sensor spectral resolution and the water vapour content knowledge. The spectral signatures of the database are then used to simulate the signal at the top of atmosphere with a radiative transfer model and to compute the integrated incident signal representing the spectral radiance measurements of the HYMAP airborne hyperspectral instrument. The sensor radiances are then corrected from the atmosphere by an atmospheric compensation tool to retrieve the surface reflectances. The SMC estimation methods are then applied on the retrieve spectral reflectances. The adaptation of the spectral index wavelengths to the HyMap sensor spectral bands and the application of the convex envelope and ISER models to boarder spectral bands lead to an error on the SMC estimation. The best performance is then obtained with the ISER model (RMSE of 2.9% and R2 of 0.96) while the four other methods lead to quite similar RMSE (from 6.4% to 7.8%) and R2 (between 0.79 and 0.83) values. In the atmosphere compensation processing, an error on the water vapour content is introduced. The most robust methods to water vapour content variations are WISOIL, NINSON, NINSOL and ISER model. The convex envelope model and NSMI index require an accurate estimation of the water vapour content in the atmosphere.

Validation of Upper-Tropospheric Humidity from SAPHIR on board Megha-Tropiques Using Tropical Soundings

AbstractThis paper describes the upper-tropospheric humidity (UTH) product derived from brightness temperature measurements of the Sondeur Atmosphérique du Profil d’Humidité Intertropicale par Radiométrie (SAPHIR) radiometer on board the Megha-Tropiques satellite. Under nonscattering conditions, the observations from three channels of SAPHIR—located at ±0.2, ±1.1, and ±2.8 GHz, respectively, around the 183.31-GHz strong water vapor absorption band—are interpreted into three different UTHs following a well-established method and thus describing the humidity content of the upper to midtroposphere. The evaluation of the UTHs is performed using reference UTHs defined from relative humidity (RH) profiles from radiosoundings of two field campaigns: the Cooperative Indian Ocean Experiment on Intraseasonal Variability in the Year 2011/Dynamics of the Madden–Julian Oscillation/Atmospheric Radiation Measurement Program Madden–Julian Oscillation Investigation Experiment (CINDY/DYNAMO/AMIE) and a Megha-Tropiques dedicated campaign in Ouagadougou, Burkina Faso, during the summer of 2012. A budget of the various uncertainties associated with each component of the evaluation method (such as the radiometric sensitivity and the radiative transfer computations) was created to achieve a more robust comparison between the two UTH estimates. The comparison between the reference UTHs and the SAPHIR UTHs reveals small global biases of lower than 2% RH on average, with correlation coefficients between 0.86 and 0.89. Taking into account the individual uncertainties gives root-mean-square errors of regressions that range between 0.92% and 4.71%. These three UTHs provide a vertical distribution of the RH that is suitable for studying various temporal and spatial scales of the tropical variability. The signature of a mesoscale convective system on its environment is briefly presented to illustrate the capability of this new dataset.

Comparison of different techniques in atmospheric temperature-humidity sensing from space

Numerical closed-loop experiments on retrieving atmospheric temperature and humidity profiles by high-resolution measurements of the outgoing thermal infrared (IR) radiation using a Russian Fourier spectrometer (IRFS-2) were performed. Three techniques were used: multiple linear regression (MLR), the iterative physical-mathematical approach (IPMA), and artificial neural networks (ANNs). The MLR technique gives significant root mean square (RMS) errors in the retrieval of the temperature profile, especially in the troposphere region; these errors may be as great as 2–3 K. The ANN and IPMA techniques are considerably more accurate, giving approximately equal RMS errors of 1.0–1.5 K at altitudes of 2–30 km. For all interpretation techniques, a growth of errors of retrieval of temperature in the lower troposphere is observed and is especially substantial (up to 3 K for the near-surface temperature) in thermal sensing over land. The systematic errors of temperature retrieval for the ANN technique are practically zero, and for the other two techniques, they do not exceed 0.4 K. The differences in thermal sensing of the atmosphere over water and land manifest themselves in the appearance of an additional five determined coefficients of expansion of the spectral dependence of the IR emissivity of land in principal components. This leads to increased errors on thermal sensing in the lower troposphere, up to ~0.5 K for all interpretation techniques. The information content of the IRFS-2 device measurements with regard to the atmospheric humidity profile is relatively small because of the values of the errors of measurements of the outgoing radiation in the shortwave range, and in particular, in the water vapour absorption band 6.3 µm. The ANN technique makes it possible to determine relative humidity in the troposphere with RMS errors of 10–15%. In the case of observations over water, the mean errors of the ANN technique are practically equal to zero, and for the MLR and IPMA techniques, they are of an approximately equal order of magnitude, namely 2–4% of relative humidity. The IPMA and MLR techniques give RMS humidity errors of 15–20% and up to 40%, respectively.

Logarithmic radiative effect of water vapor and spectral kernels

AbstractRadiative kernels have become a useful tool in climate analysis. A set of spectral kernels is calculated using a moderate resolution atmospheric transmission code MODTRAN and implemented in diagnosing spectrally decomposed global outgoing longwave radiation (OLR) changes. It is found that the effect of water vapor on the OLR is in proportion to the logarithm of its concentration. Spectral analysis discloses that this logarithmic dependency mainly results from water vapor absorption bands (0–560 cm−1 and 1250–1850 cm−1), while in the window region (800–1250 cm−1), the effect scales more linearly to its concentration. The logarithmic and linear effects in the respective spectral regions are validated by the calculations of a benchmark line‐by‐line radiative transfer model LBLRTM. The analysis based on LBLRTM‐calculated second‐order kernels shows that the nonlinear (logarithmic) effect results from the damping of the OLR sensitivity to layer‐wise water vapor perturbation by both intra‐ and inter‐layer effects. Given that different scaling approaches suit different spectral regions, it is advisable to apply the kernels in a hybrid manner in diagnosing the water vapor radiative effect. Applying logarithmic scaling in the water vapor absorption bands where absorption is strong and linear scaling in the window region where absorption is weak can generally constrain the error to within 10% of the overall OLR change for up to eightfold water vapor perturbations.