Column Water Vapor Research Articles

Fast radiative transfer parameterisation for assessing the surface solar irradiance: The Heliosat‑4 method

The new Heliosat-4 method estimates the downwelling shortwave irradiance received at ground level in all sky conditions. It provides the global irradiance and its direct and diffuse components on a horizontal plane and the direct irradiance for a plane normal to sun rays. It is a fully physical model using a fast, but still accurate approximation of radiative transfer modelling and is therefore well suited for geostationary satellite retrievals. It can also be used as a fast radiative transfer model in numerical weather prediction models. It is composed of two models based on abaci, also called look-up tables: the already-published McClear model calculating the irradiance under cloud-free conditions and the new McCloud model calculating the extinction of irradiance due to clouds. Both have been realized by using the libRadtran radiative transfer model. The main inputs to Heliosat-4 are aerosol properties, total column water vapour and ozone content as provided by the Copernicus Atmosphere Monitoring Service (CAMS) every 3 h. Cloud properties are derived from images of the Meteosat Second Generation (MSG) satellites in their 15min temporal resolution using an adapted APOLLO (AVHRR Processing scheme Over cLouds, Land and Ocean) scheme. The 15min means of irradiance estimated by Heliosat-4 are compared to corresponding measurements made at 13 stations within the Baseline Surface Radiation Network and being located in the field of view ofMSG and in various climates. The bias for global irradiance is comprised between 2 and 32Wm−2. The root mean square error (RMSE) ranges between 74 and 94Wm−2. Relative RMSE values range between 15% and 20% of the mean observed irradiance for stations in desert and Mediterranean climates, and between 26% and 43% for rainy climates with mild winters. Correlation coefficients between 0.91 and 0.97 are found. The bias for the direct irradiance at normal incidence is comprised between −163 and +50Wm−2. The RMSE ranges from 160Wm−2 (29% of the mean observed irradiance) to 288Wm−2 (63%). The correlation coefficient ranges between 0.67 and 0.87.

APDA Water Vapor Retrieval Validation for Sentinel-2 Imagery

Water vapor is one of the main parameters for atmospheric correction of Sentinel-2 imagery. Together with the aerosol retrieval it determines the accuracy of the surface reflectance product. Since Sentinel-2A and soon Sentinel-2B are operational satellites with a free data policy there is great interest in processing this data and using it for environmental studies. This contribution performs a validation of Sentinel-2 retrieved water vapor over land by comparing the scene-derived water vapor column with the AERONET measurement as an independent source. The validation is performed for a large range of AERONET site elevations and solar zenith angles using the atmospheric precorrected differential absorption (APDA) technique. Results show a high correlation with a low RMSE of about 0.1 cm for water vapor values from 0.2 to 5 cm.

Assessment of Mono- and Split-Window Approaches for Time Series Processing of LST from AVHRR—A TIMELINE Round Robin

Processing of land surface temperature from long time series of AVHRR (Advanced Very High Resolution Radiometer) requires stable algorithms, which are well characterized in terms of accuracy, precision and sensitivity. This assessment presents a comparison of four mono-window (Price 1983, Qin et al., 2001, Jiménez-Muñoz and Sobrino 2003, linear approach) and six split-window algorithms (Price 1984, Becker and Li 1990, Ulivieri et al., 1994, Wan and Dozier 1996, Yu 2008, Jiménez-Muñoz and Sobrino 2008) to estimate LST from top of atmosphere brightness temperatures, emissivity and columnar water vapour. Where possible, new coefficients were estimated matching the spectral response curves of the different AVHRR sensors of the past and present. The consideration of unique spectral response curves is necessary to avoid artificial anomalies and wrong trends when processing time series data. Using simulated data on the base of a large atmospheric profile database covering many different states of the atmosphere, biomes and geographical regions, it was assessed (a) to what accuracy and precision LST can be estimated using before mentioned algorithms and (b) how sensitive the algorithms are to errors in their input variables. It was found, that the split-window algorithms performed almost equally well, differences were found mainly in their sensitivity to input bands, resulting in the Becker and Li 1990 and Price 1984 split-window algorithm to perform best. Amongst the mono-window algorithms, larger deviations occurred in terms of accuracy, precision and sensitivity. The Qin et al., 2001 algorithm was found to be the best performing mono-window algorithm. A short comparison of the application of the Becker and Li 1990 coefficients to AVHRR with the MODIS LST product confirmed the approach to be physically sound.

Large‐eddy simulations over Germany using ICON: a comprehensive evaluation

Large‐eddy simulations (LES) with the new ICOsahedral Non‐hydrostatic atmosphere model (ICON) covering Germany are evaluated for four days in spring 2013 using observational data from various sources. Reference simulations with the established Consortium for Small‐scale Modelling (COSMO) numerical weather prediction model and further standard LES codes are performed and used as a reference. This comprehensive evaluation approach covers multiple parameters and scales, focusing on boundary‐layer variables, clouds and precipitation. The evaluation points to the need to work on parametrizations influencing the surface energy balance, and possibly on ice cloud microphysics. The central purpose for the development and application of ICON in the LES configuration is the use of simulation results to improve the understanding of moist processes, as well as their parametrization in climate models. The evaluation thus aims at building confidence in the model's ability to simulate small‐ to mesoscale variability in turbulence, clouds and precipitation. The results are encouraging: the high‐resolution model matches the observed variability much better at small‐ to mesoscales than the coarser resolved reference model. In its highest grid resolution, the simulated turbulence profiles are realistic and column water vapour matches the observed temporal variability at short time‐scales. Despite being somewhat too large and too frequent, small cumulus clouds are well represented in comparison with satellite data, as is the shape of the cloud size spectrum. Variability of cloud water matches the satellite observations much better in ICON than in the reference model. In this sense, it is concluded that the model is fit for the purpose of using its output for parametrization development, despite the potential to improve further some important aspects of processes that are also parametrized in the high‐resolution model.

Airborne observations of far-infrared upwelling radiance in the Arctic

Abstract. The first airborne measurements of the Far-InfraRed Radiometer (FIRR) were performed in April 2015 during the panarctic NETCARE campaign. Vertical profiles of spectral upwelling radiance in the range 8–50 µm were measured in clear and cloudy conditions from the surface up to 6 km. The clear sky profiles highlight the strong dependence of radiative fluxes to the temperature inversion typical of the Arctic. Measurements acquired for total column water vapour from 1.5 to 10.5 mm also underline the sensitivity of the far-infrared greenhouse effect to specific humidity. The cloudy cases show that optically thin ice clouds increase the cooling rate of the atmosphere, making them important pieces of the Arctic energy balance. One such cloud exhibited a very complex spatial structure, characterized by large horizontal heterogeneities at the kilometre scale. This emphasizes the difficulty of obtaining representative cloud observations with airborne measurements but also points out how challenging it is to model polar clouds radiative effects. These radiance measurements were successfully compared to simulations, suggesting that state-of-the-art radiative transfer models are suited to study the cold and dry Arctic atmosphere. Although FIRR in situ performances compare well to its laboratory performances, complementary simulations show that upgrading the FIRR radiometric resolution would greatly increase its sensitivity to atmospheric and cloud properties. Improved instrument temperature stability in flight and expected technological progress should help meet this objective. The campaign overall highlights the potential for airborne far-infrared radiometry and constitutes a relevant reference for future similar studies dedicated to the Arctic and for the development of spaceborne instruments.

Regional pollution and its formation mechanism over North China Plain: A case study with ceilometer observations and model simulations

AbstractTo investigate regional haze formation, ceilometer observations at the Beijing (BJ), Shijiazhuang (SJZ), Tianjin (TJ), and Qinhuangdao (QHD) stations were conducted from 12 October to 10 November 2014, to obtain the boundary layer height (BLH) and the attenuated backscattering coefficients (ABC). Particles at the four stations were highly correlated, whereas precursors of particles exhibited weaker correlations. By analyzing the typical haze episode between 21 and 26 October 2014, we found that warm advection at a height of 850 hPa from the Loess Plateau caused a gradual decline in the regional BLH. Moreover, water vapor transported from the southern NCP caused the column water vapor amount to increase from 0.015 kg m−2 to 0.042 kg m−2 in the boundary layer in BJ. As southerly transport prevailed during the transition period, ABC profiles in the BJ, TJ, and QHD stations showed a bilayer pattern, and the second layer was between 500 and 1000 m. As a result, the ABC integrations of BJ and TJ increased by 74.2 and 139.7%, respectively. During the polluted period, due to the weakened transport effect, the ABC integrations of the four stations decreased by 7.9, 18.2, 16.2, and 28.2%, respectively. Contributions of the secondary inorganic species (sulfate, nitrate, and ammonium) at BJ increased from 37.3% to 56.9%, and the mean particle size increased from 107.8 nm to 140.8 nm. Emissions in southern NCP should be mitigated during the transition period, whereas the inorganic precursors are the most important air pollutants during the polluted period.

Impact of North American intense fires on aerosol optical properties measured over the European Arctic in July 2015

AbstractIn this paper impact of intensive biomass burning (BB) in North America in July 2015, on aerosol optical and microphysical properties measured in the European Arctic, is discussed. This study was made within the framework of the Impact of Absorbing aerosols on Radiating forcing in the European Arctic project. During the BB event aerosol optical depth (AOD) at 500 nm exceeded 1.2 in Spitsbergen and 0.7 in Andenes (Norway). Angstrom exponent exceeded 1.4, while the absorbing Angstrom exponent varied between 1 and 1.25. BB aerosols were observed in humid atmosphere with a total water vapor column between 2 and 2.5 cm. In such conditions aerosols are activated and may produce clouds at different altitudes. Vertical structure of aerosol plumes over Svalbard, obtained from ceilometers and lidars, shows variability of range‐corrected signal between surface and middle and upper troposphere. Aerosol backscattering coefficients show values up to 10−5 m−1 sr−1 at 532 nm. Aerosol surface observations indicate chemical composition typical for biomass burning particles and very high single scattering properties. Scattering and absorption coefficients at 530 nm were up to 130 and 15 Mm−1, respectively. Single scattering albedo at the surface varied from 0.9 to 0.94. The averaged values over the entire atmospheric column ranged from 0.93 to 0.99. Preliminary statistics of model and Sun photometer data as well as previous studies indicate that this event, in the Arctic region, must be considered extreme (such AOD was not observed in Svalbard since 2005) with a significant impact on energy budget.

High‐accuracy measurements of total column water vapor from the Orbiting Carbon Observatory‐2

AbstractAccurate knowledge of the distribution of water vapor in Earth's atmosphere is of critical importance to both weather and climate studies. Here we report on measurements of total column water vapor (TCWV) from hyperspectral observations of near‐infrared reflected sunlight over land and ocean surfaces from the Orbiting Carbon Observatory‐2 (OCO‐2). These measurements are an ancillary product of the retrieval algorithm used to measure atmospheric carbon dioxide concentrations, with information coming from three highly resolved spectral bands. Comparisons to high‐accuracy validation data, including ground‐based GPS and microwave radiometer data, demonstrate that OCO‐2 TCWV measurements have maximum root‐mean‐square deviations of 0.9–1.3mm. Our results indicate that OCO‐2 is the first space‐based sensor to accurately and precisely measure the two most important greenhouse gases, water vapor and carbon dioxide, at high spatial resolution (1.3 × 2.3 km2) and that OCO‐2 TCWV measurements may be useful in improving numerical weather predictions and reanalysis products.

Precipitation, temperature, and moisture transport variations associated with two distinct ENSO flavors during 1979–2014

Interannual precipitation and temperature variations during 1979–2014 are investigated by examining the effects of two distinct flavors of the El Nino-Southern Oscillation (ENSO), i.e., the tropical eastern Pacific (EP) and central Pacific (CP) ENSO events. Satellite- and ground-based observations with global coverage are applied including the monthly precipitation data from the Global Precipitation Climatology Project (GPCP) and surface temperature anomalies from the NASA-GISS surface temperature anomaly analysis. Related variations in other water-cycle components including atmospheric moisture transport are also examined by using the outputs from the NASA-Modern Era Retrospective-analysis for Research and Applications (MERRA). While the second leading mode from an EOF analysis of sea surface temperature (SST) anomalies between 30°N and 30°S is dominated by interdecadal-scale variability that is not a focus of this study, the first and third leading modes represent well the EP and CP events, respectively. The corresponding principal components (PC1 and PC3) are then applied as indices to estimate the influences of the two ENSO flavors on various physical components through linear regression. Because of their distinct SST configurations in the tropical Pacific, the two ENSO flavors manifest different spatial features of precipitation anomalies as shown in past studies. Differences can also be readily seen in satellite-retrieved tropospheric layered temperatures and oceanic columnar water vapor content. General agreements between observations and MERRA outputs can be obtained as judged by consistent respective anomalies corresponding to the two ENSO flavors, suggesting that MERRA could provide an accurate account of variations on the interannual time scale. Interannual variations in MERRA vertically integrated moisture transport (VIMT) are further examined to explore possible relations between precipitation and tropospheric moisture transport corresponding to the two flavors during two contrasting seasons: December–March (DJFM) and June–September (JJAS). Anomalies of zonal moisture transport in the deep tropics following the variations in the Pacific Walker Circulation are distinctly different for two ENSO flavors and also manifest evident seasonal variations for each flavor. Differences in the zonal mean VIMT (both zonal and meridional components) are also evident between the two flavors, consistent with the differences in zonal mean precipitation anomalies from both GPCP and MERRA. Furthermore, the ENSO flavors are associated with distinct precipitation anomaly patterns over various land areas, which can be further traced to the differences in their associated VIMT anomalies, particularly during DJFM when the warm ENSO events usually reach their mature phase.

Global observations of cloud‐sensitive aerosol loadings in low‐level marine clouds

AbstractAerosol‐cloud interaction is a key component of the Earth's radiative budget and hydrological cycle, but many facets of its mechanisms are not yet fully understood. In this study, global satellite‐derived aerosol and cloud products are used to identify at what aerosol loading cloud droplet size shows the greatest sensitivity to changes in aerosol loading (ACSmax). While, on average, cloud droplet size is most sensitive at relatively low aerosol loadings, distinct spatial and temporal patterns exist. Possible determinants for these are identified with reanalysis data. The magnitude of ACSmax is found to be constrained by the total columnar water vapor. Seasonal patterns of water vapor are reflected in the seasonal patterns of ACSmax. Also, situations with enhanced turbulent mixing are connected to higher ACSmax, possibly due to intensified aerosol activation. Of the analyzed aerosol species, dust seems to impact ACSmax the most, as dust particles increase the retrieved aerosol loading without substantially increasing the concentration of cloud condensation nuclei.

Moisture‐radiative cooling instability

AbstractRadiative‐convective equilibrium (RCE)—the statistical equilibrium state of the atmosphere where convection and radiation interact in the absence of lateral transport—is widely used as a basic‐state model of the tropical atmosphere. The possibility that RCE may be unstable to development of large‐scale circulation has been raised by recent modeling, theoretical, and observational studies, and could have profound consequences for our understanding of tropical meteorology and climate. Here, we study the interaction between moisture and radiative cooling as a contributor to instability of RCE. We focus on whether the total atmospheric radiative cooling decreases with column water vapor; this condition, which we call moisture‐radiative cooling instability (MRCI), provides the potential for unstable growth of moist or dry perturbations. Analytic solutions to the gray‐gas radiative transfer equations show that MRCI is satisfied when the total column optical depth—linked to column water vapor—exceeds a critical threshold. Both the threshold and the growth rate of the instability depend strongly on the shape of the water vapor perturbation. Calculations with a realistic radiative transfer model confirm the existence of MRCI for typical tropical values of column water vapor, but show even stronger dependence on the vertical structure of water vapor perturbation. Finally, we analyze the sensitivity of atmospheric radiative cooling to variability in column water vapor in observed tropical soundings. We find that clear‐sky MRCI is satisfied across a range of locations and seasons in the real tropical atmosphere, with a partial growth rate of ∼1 month.

Convective moisture adjustment time scale as a key factor in regulating model amplitude of the Madden‐Julian Oscillation

AbstractDespite its pronounced impacts on weather extremes worldwide, the Madden‐Julian Oscillation (MJO) remains poorly represented in climate models. Here we present findings that point to some necessary ingredients to produce a strong MJO amplitude in a large set of model simulations from a recent model intercomparison project. While surface flux and radiative heating anomalies are considered important for amplifying the MJO, their strength per unit MJO precipitation anomaly is found to be negatively correlated to MJO amplitude across these multimodel simulations. However, model MJO amplitude is found to be closely tied to a model's convective moisture adjustment time scale, a measure of how rapidly precipitation must increase to remove excess column water vapor, or alternately the efficiency of surface precipitation generation per unit column water vapor anomaly. These findings provide critical insights into key model processes for the MJO and pinpoint a direction for improved model representation of the MJO.

The Seasonal Cycle of the Radiation Budget and Cloud Radiative Effect in the Amazon Rain Forest of Brazil

Abstract Changes in the climate system of the Amazon rain forest of Brazil can impact factors that influence the radiation budget such as clouds, atmospheric moisture, and the surface albedo. This study examines the relationships between clouds and radiation in this region using surface observations from the first year of the deployment of the Atmospheric Radiation Measurement (ARM) Program’s Mobile Facility 1 (AMF1) in Manacapuru, Brazil, and satellite measurements from the Clouds and the Earth’s Radiant Energy System (CERES). The seasonal cycles of the radiation budget and cloud radiative effects (CREs) are evaluated at the top of the atmosphere (TOA), at the surface, and within the atmospheric column using these observations and are placed into a regional context using the Modern-Era Retrospective Analysis for Research and Applications, version 2 (MERRA-2). Water vapor and clouds are abundant throughout the year, even though slight decreases are observed in the dry season. The column water vapor load is large enough that the longwave radiative flux divergence is nearly constant throughout the year. Clouds produce a significant shortwave CRE at the surface and TOA, exceeding 200 W m−2 during the wet season. Discrepancies, especially in column shortwave radiative absorption, between the observations and MERRA-2 are demonstrated that warrant additional analysis of the microphysical and macrophysical cloud properties in MERRA-2. More trustworthy fields in the MERRA-2 product suggest that the expansive nearby river system impacts the regional radiation budget and thereby renders AMF1 observations potentially biased relative to regions farther removed from rivers within the Amazon rain forest.

The impacts of column water vapour variability on Atlantic basin tropical cyclone activity

Column water vapour (CWV) in the Tropics is well known to affect tropical deep convection. It has been established that the tropical deep convection required for tropical cyclone formation markedly increases after reaching a critical CWV threshold. Therefore, CWV values below this threshold represent an atmosphere that is too dry for tropical cyclone formation. The interannual variability of the occurrence of this dry air and its impacts on tropical cyclone activity over the Atlantic are examined. CWV empirical orthogonal function (EOF) analysis reveals a dominant mode of interannual variability of dry air events over the tropical North Atlantic, with strong variations over the central and eastern Atlantic between 15°N and 30°N. In the positive (negative) phase of the EOF modes, tropical cyclone activity is reduced (increased) over the Atlantic basin, particularly over the main development region. On the origin of dry air, the Saharan Air Layer does not explain the CWV EOF pattern, but the pattern is shown to be related to large‐scale subsidence in association with enhanced anticyclonic vorticity. The leading mode shows significant correlations to several large‐scale climate oscillations, including the Atlantic Multi‐decadal Oscillation, Pacific Decadal Oscillation, Atlantic Meridional Mode, Pacific North American Pattern, and others. The EOF mode also indicates a relationship to the large‐scale circulation via the Hadley cell. An evaluation of the steering flow for tropical cyclones from the modulation of the subtropical high and the values of deep layer vertical wind shear is consistent with the distribution of tropical cyclone activity in only certain limited areas, suggesting that the impacts of the dry air patterns are required to provide a more complete explanation of the activity.

A Physically Constrained Calibration Database for Land Surface Temperature Using Infrared Retrieval Algorithms

Land surface temperature (LST) is routinely retrieved from remote sensing instruments using semi-empirical relationships between top of atmosphere (TOA) radiances and LST, using ancillary data such as total column water vapor or emissivity. These algorithms are calibrated using a set of forward radiative transfer simulations that return the TOA radiances given the LST and the thermodynamic profiles. The simulations are done in order to cover a wide range of surface and atmospheric conditions and viewing geometries. This study analyzes calibration strategies while considering some of the most critical factors that need to be taken into account when building a calibration dataset, covering the full dynamic range of relevant variables. A sensitivity analysis of split-windows and single channel algorithms revealed that selecting a set of atmospheric profiles that spans the full range of surface temperatures and total column water vapor combinations that are physically possible seems beneficial for the quality of the regression model. However, the calibration is extremely sensitive to the low-level structure of the atmosphere, indicating that the presence of atmospheric boundary layer features such as temperature inversions or strong vertical gradients of thermodynamic properties may affect LST retrievals in a non-trivial way. This article describes the criteria established in the EUMETSAT Land Surface Analysis—Satellite Application Facility to calibrate its LST algorithms, applied both for current and forthcoming sensors.

Deep Convection and Column Water Vapor over Tropical Land versus Tropical Ocean: A Comparison between the Amazon and the Tropical Western Pacific

Abstract The relationships between the onset of tropical deep convection, column water vapor (CWV), and other measures of conditional instability are analyzed with 2 yr of data from the DOE Atmospheric Radiation Measurement (ARM) Mobile Facility in Manacapuru, Brazil, as part of the Green Ocean Amazon (GOAmazon) campaign, and with 3.5 yr of CWV derived from global positioning system meteorology at a nearby site in Manaus, Brazil. Important features seen previously in observations over tropical oceans—precipitation conditionally averaged by CWV exhibiting a sharp pickup at high CWV, and the overall shape of the CWV distribution for both precipitating and nonprecipitating points—are also found for this tropical continental region. The relationship between rainfall and CWV reflects the impact of lower-free-tropospheric moisture variability on convection. Specifically, CWV over land, as over ocean, is a proxy for the effect of free-tropospheric moisture on conditional instability as indicated by entraining plume calculations from GOAmazon data. Given sufficient mixing in the lower troposphere, higher CWV generally results in greater plume buoyancies through a deep convective layer. Although sensitivity of buoyancy to other controls in the Amazon is suggested, such as boundary layer and microphysical processes, the CWV dependence is consistent with the observed precipitation onset. Overall, leading aspects of the relationship between CWV and the transition to deep convection in the Amazon have close parallels over tropical oceans. The relationship is robust to averaging on time and space scales appropriate for convective physics but is strongly smoothed for averages greater than 3 h or 2.5°.

Validation and update of OMI Total Column Water Vapor product

Abstract. The collection 3 Ozone Monitoring Instrument (OMI) Total Column Water Vapor (TCWV) data generated by the Smithsonian Astrophysical Observatory's (SAO) algorithm version 1.0 and archived at the Aura Validation Data Center (AVDC) are compared with NCAR's ground-based GPS data, AERONET's sun-photometer data, and Remote Sensing System's (RSS) SSMIS data. Results show that the OMI data track the seasonal and interannual variability of TCWV for a wide range of climate regimes. During the period from 2005 to 2009, the mean OMI−GPS over land is −0.3 mm and the mean OMI−AERONET over land is 0 mm. For July 2005, the mean OMI−SSMIS over the ocean is −4.3 mm. The better agreement over land than over the ocean is corroborated by the smaller fitting residuals over land and suggests that liquid water is a key factor for the fitting quality over the ocean in the version 1.0 retrieval algorithm. We find that the influence of liquid water is reduced using a shorter optimized retrieval window of 427.7–465 nm. As a result, the TCWV retrieved with the new algorithm increases significantly over the ocean and only slightly over land. We have also made several updates to the air mass factor (AMF) calculation. The updated version 2.1 retrieval algorithm improves the land/ocean consistency and the overall quality of the OMI TCWV data set. The version 2.1 OMI data largely eliminate the low bias of the version 1.0 OMI data over the ocean and are 1.5 mm higher than RSS's “clear” sky SSMIS data in July 2005. Over the ocean, the mean of version 2.1 OMI−GlobVapour is 1 mm for July 2005 and 0 mm for January 2005. Over land, the version 2.1 OMI data are about 1 mm higher than GlobVapour when TCWV < 15 mm and about 1 mm lower when TCWV > 15 mm.

Emulation of Leaf, Canopy and Atmosphere Radiative Transfer Models for Fast Global Sensitivity Analysis

Physically-based radiative transfer models (RTMs) help understand the interactions of radiation with vegetation and atmosphere. However, advanced RTMs can be computationally burdensome, which makes them impractical in many real applications, especially when many state conditions and model couplings need to be studied. To overcome this problem, it is proposed to substitute RTMs through surrogate meta-models also named emulators. Emulators approximate the functioning of RTMs through statistical learning regression methods, and can open many new applications because of their computational efficiency and outstanding accuracy. Emulators allow fast global sensitivity analysis (GSA) studies on advanced, computationally expensive RTMs. As a proof-of-concept, three machine learning regression algorithms (MLRAs) were tested to function as emulators for the leaf RTM PROSPECT-4, the canopy RTM PROSAIL, and the computationally expensive atmospheric RTM MODTRAN5. Selected MLRAs were: kernel ridge regression (KRR), neural networks (NN) and Gaussian processes regression (GPR). For each RTM, 500 simulations were generated for training and validation. The majority of MLRAs were excellently validated to function as emulators with relative errors well below 0.2%. The emulators were then put into a GSA scheme and compared against GSA results as generated by original PROSPECT-4 and PROSAIL runs. NN and GPR emulators delivered identical GSA results, while processing speed compared to the original RTMs doubled for PROSPECT-4 and tripled for PROSAIL. Having the emulator-GSA concept successfully tested, for six MODTRAN5 atmospheric transfer functions (outputs), i.e., direct and diffuse at-surface solar irradiance ( E d i f , E d i r ), direct and diffuse upward transmittance ( T d i r , T d i f ), spherical albedo (S) and path radiance ( L 0 ), the most accurate MLRA’s were subsequently applied as emulator into the GSA scheme. The sensitivity analysis along the 400–2500 nm spectral range took no more than a few minutes on a contemporary computer—in comparison, the same analysis in the original MODTRAN5 would have taken over a month. Key atmospheric drivers were identified, which are on the one hand aerosol optical properties, i.e., aerosol optical thickness (AOT), Angstrom coefficient (AMS) and scattering asymmetry variable (G), mostly driving diffuse atmospheric components, E d i f and T d i f ; and those affected by atmospheric scattering, L 0 and S. On the other hand, as expected, AOT, AMS and columnar water vapor (CWV) in the absorption regions mostly drive E d i r and T d i r atmospheric functions. The presented emulation schemes showed very promising results in replacing costly RTMs, and we think they can contribute to the adoption of machine learning techniques in remote sensing and environmental applications.

Uncertainty Characteristics of Total Water Path Retrievals in Shallow Cumulus Derived from Spaceborne Radar/Radiometer Integral Constraints

AbstractA precipitating marine cumulus cloud simulation is coupled to radiation propagation models to simulate active and passive microwave observations at 94 GHz. The simulations are used to examine the error characteristics of the total water path retrieved from the integral constraints of the passive microwave brightness temperature or the path-integrated attenuation (PIA) using a spatial interpolation technique. Three sources of bias are considered: 1) the misdetection of cloudy pixels as clear, 2) the systematic differences in the column water vapor between cloudy and clear skies, and 3) the nonuniform beamfilling effects on the observables. The first two sources result in biases on the order of 5–10 g m−2 of opposite signs that tend to cancel. The third source results in a bias that increases monotonically with the water path that approaches 50%. Nonuniform beamfilling is sensitive to footprint size. Random error results from both instrument measurement precision and the natural variability in the relationship between the water path and the observables. Random errors for the retrievals using the CloudSat PIA are estimated to be the larger of either 20 g m−2 or 30%. A radar/radiometer system with a measurement precision of 0.3 K or 0.05 dB could reduce this error to the larger of either 10 g m−2 or 30%. All error mechanisms reported here result from variability in either the spatial structure of the atmosphere or the hydrometeor drop size distribution. The results presented here are specific to the cloud simulation and in general the magnitude will vary globally.

Representativeness of total column water vapour retrievals from instruments on polar orbiting satellites

Abstract. The remote sensing of total column water vapour (TCWV) from polar orbiting, sun-synchronous satellite spectrometers such as the Medium Resolution Imaging Spectrometer (MERIS) on board of ENVISAT and the Moderate Imaging Spectroradiometer (MODIS) on board of Aqua and Terra enables observations on a high spatial resolution and a high accuracy over land surfaces. The observations serve studies about small-scale variations of water vapour as well as the detection of local and global trends. However, depending on the swath width of the sensor, the temporal sampling is low and the observations of TCWV are limited to cloud-free land scenes. This study quantifies the representativeness of a single TCWV observation at the time of the satellite overpass under cloud-free conditions by investigating the diurnal cycle of TCWV using 9 years of a 2-hourly TCWV data set from global GNSS (Global Navigation Satellite Systems) stations. It turns out that the TCWV observed at 10:30 local time (LT) is generally lower than the daily mean TCWV by 0.65 mm (4 %) on average for cloud-free cases. Averaging over all GNSS stations, the monthly mean TCWV at 10:30 LT, constrained to cases that are cloud-free, is 5 mm (25 %) lower than the monthly mean TCWV at 10:30 LT of all cases. Additionally, the diurnal variability of TCWV is assessed. For the majority of GNSS stations, the amplitude of the averaged diurnal cycle ranges between 1 and 5 % of the daily mean with a minimum between 06:00 and 10:00 LT and maximum between 16:00 and 20:00 LT. However, a high variability of TCWV on an individual day is detected. On average, the TCWV standard deviation is about 15 % regarding the daily mean.