Column Water Vapor Amount Research Articles

Validating the accuracy of SO2 gas retrievals in the thermal infrared (8–14 μm)

Quantifying sulfur dioxide (SO2) in volcanic plumes is important for eruption predictions and public health. Ground-based remote sensing of spectral radiance of plumes contains information on the path-concentration of SO2. However, reliable inversion algorithms are needed to convert plume spectral radiance measurements into SO2 path-concentrations. Various techniques have been used for this purpose. Recent approaches have employed thermal infrared (TIR) imaging between 8 μm and 14 μm to provide two-dimensional mapping of plume SO2 path-concentration, using what might be described as “dual-view” techniques. In this case, the radiance (or its surrogate brightness temperature) is computed for portions of the image that correspond to the plume and compared with spectral radiance obtained for adjacent regions of the image that do not (i.e., “clear sky”). In this way, the contribution that the plume makes to the measured radiance can be isolated from the background atmospheric contribution, this residual signal being converted to an estimate of gas path-concentration via radiative transfer modeling. These dual-view approaches suffer from several issues, mainly the assumption of clear sky background conditions. At this time, the various inversion algorithms remain poorly validated. This paper makes two contributions. Firstly, it validates the aforementioned dual-view approaches, using hyperspectral TIR imaging data. Secondly, it introduces a new method to derive SO2 path-concentrations, which allows for single point SO2 path-concentration retrievals, suitable for hyperspectral imaging with clear or cloudy background conditions. The SO2 amenable lookup table algorithm (SO2–ALTA) uses the MODTRAN5 radiative transfer model to compute radiance for a variety (millions) of plume and atmospheric conditions. Rather than searching this lookup table to find the best fit for each measured spectrum, the lookup table was used to train a partial least square regression (PLSR) model. The coefficients of this model are used to invert measured radiance spectra to path-concentration on a pixel-by-pixel basis. In order to validate the algorithms, TIR hyperspectral measurements were carried out by measuring sky radiance when looking through gas cells filled with known amounts of SO2. SO2–ALTA was also tested on retrieving SO2 path-concentrations from the Kīlauea volcano, Hawai’i. For cloud-free conditions, all three techniques worked well. In cases where background clouds were present, then only SO2–ALTA was found to provide good results, but only under low atmospheric water vapor column amounts.

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.

Mars’ water vapor mapping by the SPICAM IR spectrometer: Five martian years of observations

The SPICAM IR instrument on the Mars Express mission continuously observes the water vapor in the martian atmosphere starting from 2004 in the 1.38-μm spectral band. The water vapor column abundance is retrieved from nadir observations to characterize its spatial, seasonal and interannual variations. A reference set of SPICAM water vapor column abundances (zonally averaged) covering the time period from 2004 to 2013 (martian years 27–31) is available for a grid of 2° Ls×2° latitude, along with an average reference map of water vapor abundance combining all the martian years of Mars Express observations. Compared to the previous data retrieval by Fedorova et al. (Fedorova, A., Korablev, O., Bertaux, J.L., Rodin, A., Kiselev, A., Perrier, S. [2006]. J. Geophys. Res. 111, E09S08) the new processing algorithm includes many improvements concerning the calibration and assumed parameters. A major improvement is the account for aerosol scattering based on dust and water ice cloud optical depths measured by THEMIS/Mars Odyssey (Smith, M.D. [2009]. Icarus 202, 444–452). The account for multiple scattering by aerosol particles increases the retrieved water vapor amount by ∼10% in polar areas during summer, and up to 60–70% for large solar zenith angles. The sensitivity of the results to aerosol properties, surface albedo, solar spectrum, and water vapor vertical distribution has also been studied. The retrieved water vapor reveals nominal annual cycle with maximum abundance of about 60–70pr.μm for the Northern summer and ∼20pr.μm for the Southern summer. The annual average amount of water has been estimated to be of 10–20pr.μm, in agreement with other measurements. From year to year the seasonal cycle of water vapor abundance is very stable. An observed decrease during the MY 28 global dust storm cannot be fully attributed to the masking effect of dust, and indicates a real decrease of water amount near or above the surface. No evidence of diurnal variation of column water vapor amount was found, even though the 1.38-μm measurements are sensitive to the few lowermost kilometers above the surface.

Validation of two independent retrievals of SCIAMACHY water vapour columns using radiosonde data

Abstract. Two independently derived SCIAMACHY total water vapour column (WVC) products are compared with integrated water vapour data calculated from radiosonde measurements, and with each other. The two SCIAMACHY WVC products are retrieved with two different retrieval algorithms applied in the visible and short-wave infrared wavelength regions respectively. The first SCIAMACHY WVC product used in the comparison is ESA's level 2 version 5.01 WVC product derived with the Air Mass Corrected Differential Optical Absorption Spectroscopy (AMC-DOAS) retrieval algorithm applied in the visible wavelength range (SCIAMACHY-ESA). The second SCIAMACHY WVC product is derived using the iterative maximum likelihood method (IMLM) in the short-wave infrared wavelength range and developed by Netherlands Institute for Space Research (SCIAMACHY-IMLM). Both SCIAMACHY WVC products are compared with collocated water vapour amounts determined from daily relative humidity radiosonde measurements obtained from the European Centre for Medium-Range Weather Forecasts (ECMWF) radiosonde network. The SCIAMACHY-ESA WVC product is compared with radiosonde-derived WVC amounts for an 18-month period from February 2010 to mid-August 2011, and the SCIAMACHY-IMLM WVC amounts are compared with radiosonde WVC amounts for the two individual years of 2004 and 2009. In addition the WVC amounts from SCIAMACHY-ESA and SCIAMACHY-IMLM are also compared with each other for a 1-month period for June 2009. The AMC-DOAS method used to retrieve SCIAMACHY-ESA WVC is able to correct for water vapour present below the clouds and can be used during cloudy conditions over both land and ocean surfaces. Results indicate a good agreement between the WVC amounts of SCIAMACHY-ESA and that of radiosondes, with a mean difference of −0.32 g cm−2 for all collocated cases. Overall the SCIAMACHY-ESA WVC amounts are smaller than the radiosonde WVC amounts, especially over oceans. For cloudy conditions the WVC bias has a clear dependence on the cloud top height and increases with increasing cloud top heights larger than approximately 2 km. A likely cause for this could be the different vertical profile shapes of water vapour and O2 leading to different relative changes in their optical thickness, which makes the air mass factor (AMF) correction method used in the algorithm less suitable for high clouds. The SCIAMACHY-IMLM product's water vapour measurements are best used over land surfaces during cloud-free conditions, and in these cases a good agreement is found when compared to radiosonde WVC amounts, with a mean difference of 0.08 g cm−2. It is shown that over ocean surfaces during cloudy conditions the partial SCIAMACHY-IMLM water vapour column above the cloud can be well estimated by using the simultaneously retrieved methane column to calculate the cloud top height. Comparing the two satellite WVC products with each other indicates that SCIAMACHY-ESA consistently measures higher WVC amounts than those of SCIAMACHY-IMLM. Furthermore, the importance of the choice of cloud product is highlighted, as intercomparisons between the two SCIAMACHY WVC products indicate that using different cloud products to screen water vapour data for cloud-free conditions influences the data selection and may ultimately lead to a variation in results. In the last section of the paper, various options for filtering the two SCIAMACHY WVC data sets are discussed and best selection criteria suggested.

A new approach to retrieving cirrus cloud height with a combination of MODIS 1.24‐ and 1.38‐μm channels

An approach is developed for inferring cloud top height (CTH) by using two shortwave infrared (SWIR) channels (i.e., 1.24‐ and 1.38‐μm) with similar cloud scattering and absorption properties but very different water vapor absorption properties. This channel combination is used to accurately infer the column water vapor amount above the clouds, from which the CTH can be retrieved. The approach performs best for ice clouds located in the upper troposphere. For those clouds, our approach performs as well or better than the current operational cloud height retrieval algorithm adopted by the MODIS science team.

A sub-pixel-based calculation of fire radiative power from MODIS observations: 2. Sensitivity analysis and potential fire weather application

Fire radiative power (FRP) over a pixel area has been highlighted as a valuable parameter for quantitatively deriving smoke emissions. However, smoke plume rise forecasts and characterizations of fire intensity require additional information, including the FRP over the fire area (FRPf) and per fire area (or fire radiative flux), both of which can be calculated through a bi-spectral retrieval of sub-pixel fire area and temperature. This study, the second in a two-part series, examines the sources of error and the corresponding uncertainties in a sub-pixel algorithm that calculates FRPf for fire pixels identified at 1km2 nominal spatial resolution by the MODerate Resolution Imaging Spectroradiometer (MODIS) fire detection algorithm (collection 5). Radiative transfer simulations are incorporated to account for atmospheric effects as a function of Earth-satellite geometry at 3.96 and 11μm (MODIS fire detection channels), and show that the 11μm channel is highly sensitive to variations in column water vapor amount. By investigating several fire events in California, considerable variations in retrieved fire area, occasionally by more than 50%, are observed when comparing the mid-latitude summer climatology and observation-based atmospheric profiles that are used in the sub-pixel retrieval algorithm. While regions of dry, brown vegetation may also increase the potential for error via the surface emissivity assumption, the algorithm is much more sensitive to errors in 11μm background brightness temperature, where an error of only 1.0K may alter the retrieved fire area by an order of magnitude or more. An independent application, using the Texas wildfire event of September 2011, reveals that the sub-pixel retrieval can even become irrelevant for 17.6% of the available MODIS fire pixels as a result of noise in the background region causing the 11μm background brightness temperature to become warmer than the fire pixel, especially during daytime scenes. The various sources of uncertainty in the estimates of FRPf and fire area can be reduced through the summation of individual pixel-level retrievals for large clusters of fire pixels, which can be defined based on the resolution of a mesoscale model grid. In comparison to the standard MODIS pixel-based FRP, the flux of FRPf, defined as total FRPf divided by the retrieved fire area, is shown to have a stronger and statistically significant correlation with surface (10-meter) wind speed and air temperature, especially for large fire pixel clusters, where the respective linear correlation coefficients are 0.55 and 0.77. These strong relationships suggest that, while additional studies are warranted, the flux of FRPf may offer the potential for improved characterizations of the meteorological effects on fire intensity compared to the standard pixel-based FRP.

Improvements of top-of-atmosphere and surface irradiance computations with CALIPSO-, CloudSat-, and MODIS-derived cloud and aerosol properties

[1] One year of instantaneous top-of-atmosphere (TOA) and surface shortwave and longwave irradiances are computed using cloud and aerosol properties derived from instruments on the A-Train Constellation: the Cloud-Aerosol Lidar with Orthogonal Polarization (CALIOP) on the Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observation (CALIPSO) satellite, the CloudSat Cloud Profiling Radar (CPR), and the Aqua Moderate Resolution Imaging Spectrometer (MODIS). When modeled irradiances are compared with those computed with cloud properties derived from MODIS radiances by a Clouds and the Earth's Radiant Energy System (CERES) cloud algorithm, the global and annual mean of modeled instantaneous TOA irradiances decreases by 12.5 W m−2 (5.0%) for reflected shortwave and 2.5 W m−2 (1.1%) for longwave irradiances. As a result, the global annual mean of instantaneous TOA irradiances agrees better with CERES-derived irradiances to within 0.5W m−2 (out of 237.8 W m−2) for reflected shortwave and 2.6W m−2 (out of 240.1 W m−2) for longwave irradiances. In addition, the global annual mean of instantaneous surface downward longwave irradiances increases by 3.6 W m−2 (1.0%) when CALIOP- and CPR-derived cloud properties are used. The global annual mean of instantaneous surface downward shortwave irradiances also increases by 8.6 W m−2 (1.6%), indicating that the net surface irradiance increases when CALIOP- and CPR-derived cloud properties are used. Increasing the surface downward longwave irradiance is caused by larger cloud fractions (the global annual mean by 0.11, 0.04 excluding clouds with optical thickness less than 0.3) and lower cloud base heights (the global annual mean by 1.6 km). The increase of the surface downward longwave irradiance in the Arctic exceeds 10 W m−2 (∼4%) in winter because CALIOP and CPR detect more clouds in comparison with the cloud detection by the CERES cloud algorithm during polar night. The global annual mean surface downward longwave irradiance of 345.4 W m−2 is estimated by combining the modeled instantaneous surface longwave irradiance computed with CALIOP and CPR cloud profiles with the global annual mean longwave irradiance from the CERES product (AVG), which includes the diurnal variation of the irradiance. The estimated bias error is −1.5 W m−2 and the uncertainty is 6.9 W m−2. The uncertainty is predominately caused by the near-surface temperature and column water vapor amount uncertainties.

Near-Real-Time Ocean Color Data Processing Using Ancillary Data From the Global Forecast System Model

This paper investigates the improvements to the quality of ocean color data using an appropriate choice of ancillary data for National Oceanic and Atmospheric Administration (NOAA) operational ocean color data processing, which requires routine ocean color product production in near real time. The ancillary data, such as the total column ozone amount, sea surface wind speed, atmospheric pressure, and total column water-vapor amount, are required for satellite ocean color data processing for deriving ocean color products, e.g., normalized water-leaving radiance spectra data, chlorophyll-a concentration, water diffuse attenuation coefficient, etc. Currently, NOAA's CoastWatch program uses the climatology ancillary data for the near-real-time ocean color data processing. Alternative ancillary data sets that can replace the climatology data for the near-real-time ocean color data processing have been investigated and studied. The studies were carried out from four selected NOAA CoastWatch regions covering the U.S. coastal and Hawaii regions and for four months (January, April, July, and October), representing the four seasons. Based on the evaluation results, we propose to use the ancillary data produced from the Global Forecast System (GFS) model for the NOAA operational ocean color data processing, as well as for any other near-real-time data processing that requires ancillary data inputs. The effects of using the GFS model data on the accuracy of the derived ocean color products are also investigated and discussed.

A far‐infrared radiative closure study in the Arctic: Application to water vapor

Far‐infrared (λ > 15.0 μm) (far‐IR) radiative processes provide a large fraction of Earth's outgoing longwave radiation and influence upper tropospheric vertical motion. Water vapor, because of its abundance and strong absorption properties over an extended spectral range, is the primary source of these radiative processes. Historically, the lack of spectrally resolved radiometric instruments and the opacity of the lower atmosphere have precluded extensive studies of far‐IR water vapor absorption properties. The U.S. Department of Energy Atmospheric Radiation Measurement (ARM) program has organized a series of field experiments, the Radiative Heating in Underexplored Bands Campaigns (RHUBC), to address this deficiency. The first phase of RHUBC took place in 2007 at the ARM North Slope of Alaska Climate Research Facility. Measurements taken before and during this campaign have provided the basis for a clear‐sky radiative closure study aimed at reducing key uncertainties associated with far‐IR radiative transfer models. Extended‐range Atmospheric Emitted Radiance Interferometer infrared radiance observations taken in clear sky conditions were compared against calculations from the Line‐By‐Line Radiative Transfer Model. The water vapor column amounts used in these calculations were retrieved from 183 GHz radiometer measurements. The uncertainty in these integrated water vapor retrievals is approximately 2%, a notable improvement over past studies. This far‐IR radiative closure study resulted in an improvement to the Mlawer‐Tobin Clough‐Kneiyzs‐Davies (MT_CKD) water vapor foreign continuum model and updates to numerous, far‐IR water vapor line parameters from their values in the circa 2006 version of the HITRAN molecular line parameter database.

Solar radiation budget and radiative forcing due to aerosols and clouds

This study integrates global data sets for aerosols, cloud physical properties, and shortwave radiation fluxes with a Monte Carlo Aerosol‐Cloud‐Radiation (MACR) model to estimate both the surface and the top‐of‐atmosphere (TOA) solar radiation budget as well as atmospheric column solar absorption. The study also quantifies the radiative forcing of aerosols and that of clouds. The observational input to MACR includes data from the Multiangle Imaging Spectroradiometer (MISR) for aerosol optical depths, single scattering albedos, and asymmetry factors; satellite retrieved column water vapor amount; the Total Ozone Mapping Spectrometer (TOMS) total ozone amount; and cloud fraction and cloud optical depth from the Cloud and Earth's Radiant Energy System (CERES) cloud data. The present radiation budget estimates account for the diurnal variation in cloud properties. The model was validated against instantaneous, daily and monthly solar fluxes from the ground‐based Baseline Surface Radiation Network (BSRN) network, the Global Energy Balance Archive (GEBA) surface solar flux data, and CERES TOA measurements. The agreement between simulated and observed values are within experimental errors, for all of the cases considered here: instantaneous fluxes and monthly mean fluxes at stations around the world and TOA fluxes and cloud forcing for global annual mean and zonal mean fluxes; in addition the estimated aerosol forcing at TOA also agrees with other observationally derived estimates. Overall, such agreements suggest that global data sets of aerosols and cloud parameters released by recent satellite experiments (MISR, MODIS and CERES) meet the required accuracy to use them as input to simulate the radiative fluxes within instrumental errors. Last, the atmospheric solar absorption derived in this study should be treated as an improved estimate when compared with earlier published studies. The main source of improvement in the present estimate is the use of global distribution of observed parameters for model input such as aerosols and clouds. The agreement between simulated and observed solar fluxes at the surface supports our conclusion that the present estimate is an improvement over previous studies. MACR with the global input data was used to simulate the global and regional solar radiation budget, aerosol radiative forcing and cloud radiative forcing for a 3‐year period from 2000 to 2002. We estimate the planetary albedo for a 3‐year average to be 28.9 ± 1.2% to be compared with CERES estimate of 28.6% and ERBE's estimate of 29.6%. Without clouds (including aerosols) the planetary albedo is only 15.0 ± 0.6%. The global mean TOA shortwave cloud forcing is −47.5 ± 4 W m−2, comparing well with the CERES and ERBE estimates of −46.5 and −48 W m−2, respectively. The clear‐sky atmospheric absorption is 72 ± 3 W m−2, and the surface absorption is 218 ± 4 W m−2. Clouds in all‐sky conditions enhance atmospheric absorption from 72 ± 3 W m−2 to 79 ± 5 W m−2 and decrease surface solar absorption from 218 ± 4 W m−2 to 164 ± 6 W m−2. The present estimate of 79 W m−2 for all‐sky solar absorption is much larger than the Intergovernmental Panel on Climate Change (2001) values of about 67 W m−2. Most of the increased atmospheric solar absorption is due to improved treatment of aerosol absorption (backed by surface based aerosol network and chemical transport models) and water vapor spectroscopic data. The global mean clear‐sky aerosol (both natural and anthropogenic) radiative forcing at the TOA and the surface are −6.0 ± 1 W m−2 and −11.0 ± 2 W m−2, respectively. In the presence of clouds the aerosol radiative forcing is −3.0 ± 1 W m−2 (at TOA) and −7.0 ± 2 W m−2 (at the surface). The study also documents the significant regional variations in the solar radiation budget and radiative forcing of aerosols and clouds.

Clear‐sky column water vapor retrievals using the Airborne Imaging Microwave Radiometer (AIMR)

The brightness temperature measurements from the Airborne Imaging Microwave Radiometer (AIMR) microwave radiometer 37 GHz and 90 GHz channels are used to determine the column water vapor amount under clear‐sky conditions during the Indian Ocean Experiment (INDOEX) campaign in 1999. The retrieval algorithm is based on the integrated relative humidity profiles measured by numerous dropsondes released from the National Center for Atmospheric Research (NCAR) C‐130 aircraft that operated during the 2 month Intensive Field Phase (IFP) of INDOEX. The highest correlations of the linear relationship between the AIMR brightness temperature measurements and the dropsonde column water vapor values are for the nadir brightness temperatures of the 37 GHz and 90 GHz AIMR channels. The column water vapor, w, given with units of kg m−2, may be expressed in terms of the 90 GHz nadir brightness temperature, TB, via the expression, w = −209.4 + 1.025 TB, whereas in terms of the 37 GHz AIMR nadir brightness temperature, w = −277.8 + 1.835 TB. These expressions are valid when AIMR brightness temperature measurements about the NCAR C‐130 are made at high altitudes (above 5 km) and when the atmospheric column is cloud‐free below the aircraft. Other relationships between the polarized components of the AIMR brightness temperature measurements as well as various combinations (differences) of these polarized components with the column water vapor did not identify any parameterizations to the algorithm better than that seen with the mean of the raw brightness temperature components.

Validation of the Moderate‐Resolution Imaging Spectroradiometer precipitable water vapor product using measurements from GPS on the Tibetan Plateau

Moderate‐Resolution Imaging Spectroradiometer (MODIS) Global atmospheric column water vapor amount product (MOD05_L2) released by NASA Goddard DAAC was evaluated using 1 year of observations obtained by the Global Positioning System (GPS) installed at Gaize (32.3°N, 84.06°E, 4420 m) and Naqu (30.48°N, 92.06°E, 4518 m) on the Tibetan Plateau. It was found that the MODIS retrieved atmospheric column water vapor amount is much lower than that retrieved by GPS at the two sites. The results show that there is a systematic bias in precipitable water between MODIS and GPS, with root‐mean‐square errors (rms) of 2.93 mm at Naqu and 3.48 mm at Gaize. Mean relative differences in PW between GPS and MODIS at Naqu and Gaize are 57.1% and 51.1%, respectively. The reason for the bias is found to be an error in the coding of operational water vapor retrievals at the NASA Goddard DAAC for data processing. After correction for the MODIS water vapor product (MOD05_L2), the RMS difference between GPS‐PW and MODIS‐PW drops to 1.9 mm at Naqu and 1.68 mm at Gaize.

Use of shadows to retrieve water vapor in hazy atmospheres

Techniques aimed at retrieving water vapor from satellite data of reflected near-infrared solar radiation have progressed significantly in recent years. These techniques rely on observation of water vapor attenuation of near-infrared solar radiation reflected by the Earth's surface. Ratios of measured radiances at wavelengths inside and outside water vapor absorbing channels are used for retrieval purposes. These ratios partially remove the dependence of surface reflectance on wavelength and are used to retrieve the total column water vapor amount. Hazy atmospheric conditions, however, introduce errors into this widely used technique. A new method based on radiance differences between clear and nearby shadowed surfaces, combined with ratios between water vapor absorbing and window regions, is presented that improves water vapor retrievals under hazy atmospheric conditions. Radiative transfer simulations are used to demonstrate the advantage offered by this technique.

Satellite observations of the water vapor greenhouse effect and column longwave cooling rates: Relative roles of the continuum and vibration‐rotation to pure rotation bands

The Clouds and the Earth's Radiant Energy System (CERES) instrument on board the Tropical Rainfall Measuring Mission (TRMM) satellite provides, for the first time, a large‐scale (40 S to 40 N) data set for the atmospheric greenhouse effect and the column‐averaged longwave (LW) radiative cooling rates in the broadband (5–100 microns) and the window (8–12 microns) regions. We demonstrate here that the separation into the window and the nonwindow (5–8 microns and 12–100 microns) fluxes provides the first global‐scale data set to exhibit the sensitivity of the atmospheric greenhouse effect to vertical water vapor distribution. The nonwindow greenhouse effect varies linearly with the logarithm of column water vapor amount weighted with the atmospheric pressure, while the window component varies quadratically with the water vapor partial pressure. The column cooling rates range from about −170 to −210 W m−2 for the nonwindow region. The window cooling rates are only about 10% to 20% of the above range and approach rapidly to near‐zero values for surface temperatures less than 288 K. The nonwindow component of the greenhouse effect and cooling rates are shown to be more sensitive to upper troposphere water vapor, while the window greenhouse effect and cooling rates are shown to be more sensitive to the lower troposphere water vapor amount. In addition, the data reveal that in tropical regions, with warm sea surface temperatures (greater than 297 K) and elevated upper tropospheric water vapor amounts, the continuum emission in the window region leads to enhanced cooling of the column, while the rotational bands in the nonwindow region lead to a net decrease in the longwave cooling of the atmospheric column.

First retrieval of global water vapour column amounts from SCIAMACHY measurements

Abstract. Global water vapour column amounts have been derived for the first time from measurements of the SCanning Imaging Absorption spectroMeter for Atmospheric CHartographY (SCIAMACHY) on the European environmental satellite ENVISAT. For this purpose, two different existing retrieval algorithms have been adapted, namely the Air Mass Corrected Differential Absorption Spectroscopy (AMC-DOAS) which was originally designed for GOME and the Weighting Function Modified Differential Absorption Spectroscopy (WFM-DOAS) which was mainly designed for the retrieval of CH4, CO2 and CO from SCIAMACHY near-infrared spectra. Here, both methods have been applied to SCIAMACHY's nadir measurements in the near-visible spectral region around 700 nm. Taking into account a systematic offset of 10%, the results of these two methods agree within a scatter of about ±0.5 g/cm2 with corresponding SSM/I and ECMWF water vapour data. This deviation includes contributions from the temporal and spatial variability of water vapour. In fact, the mean deviation between the SCIAMACHY and the correlative data sets is much smaller: the SCIAMACHY total water vapour columns are typically about 0.15 g/cm2 lower than the SSM/I values and less than 0.1 g/cm2 lower than corresponding ECMWF data. The SCIAMACHY water vapour results agree well with correlative data not only over ocean but also over land, thus showing the capability of SCIAMACHY to derive water vapour concentrations on the global scale.

Water vapor retrievals using Moderate Resolution Imaging Spectroradiometer (MODIS) near‐infrared channels

At present, two Moderate Resolution Imaging Spectroradiometer (MODIS) instruments on board the NASA Terra and Aqua Spacecraft platforms are operational for global remote sensing of the land, ocean, and atmosphere. In this paper, we describe an algorithm for water vapor derivations using several MODIS near‐IR channels. The derivations are made over areas that have reflective surfaces in the near‐IR, such as clear land areas, clouds, and oceanic areas with Sun glint. The algorithm relies on observations of water vapor attenuation of near‐IR solar radiation reflected by surfaces and clouds. Techniques employing ratios of water vapor absorbing channels centered near 0.905, 0.936, and 0.940 μm with atmospheric window channels at 0.865 and 1.24 μm are used. The ratios partially remove the effects of variation of surface reflectance with wavelengths and result in the atmospheric water vapor transmittances. The column water vapor amounts are derived from the transmittances based on theoretical calculations and using lookup table procedures. Typical errors in the derived water vapor values are in the range between 5% and 10%. The daily “pixel‐based” near‐IR water vapor product, which is a standard MODIS level 2 data product, at the 1‐km spatial resolution of the MODIS instrument, and the daily, 8‐day, and monthly near‐IR water vapor products, which are standard MODIS level 3 products, at a 1° by 1° latitude‐longitude grid globally are now routinely produced at a NASA computing facility. We present samples of water vapor images and comparisons to ground‐based measurements by microwave radiometers.

Retrieval of total water vapour column amounts from GOME/ERS-2 data

A method for the retrieval of vertical column amounts of atmospheric water vapour from measurements of the Global Ozone Monitoring Experiment (GOME) in the visible spectral region is presented. Originally developed for the determination of global ozone concentrations, the GOME instrument is operating successfully on ERS-2 since 1995, providing information also on several other atmospheric constituents like NO 2, BrO, OClO, H 2CO, SO 2, and recently also H 2O. The method for the derivation of water vapour amounts is based on the Differential Optical Absorption Spectroscopy (DOAS) approach, which has been extended to take into account effects arising from a strongly wavelength dependent absorption. Further improvements of the algorithm now allow to retrieve water vapour column densities under all cloud conditions. First results show an acceptable agreement between total water vapour columns derived from GOME measurements and H 2O columns obtained from the Special Sensor Microwave Imager SSM/I data, although the scatter of the GOME data is high, which needs to be further investigated.

Identification of clear skies from broadband pyranometer measurements and calculation of downwelling shortwave cloud effects

We present an automated method to identify periods of clear skies for a 160° field of view using only 1‐min measurements of surface downwelling total and diffuse shortwave irradiance. The clear‐sky detection method is verified using Whole Sky Imager and lidar data, observer reports, and model comparisons. Identified clear‐sky irradiance measurements are then used to empirically fit clear‐sky irradiance functions using the cosine of the solar zenith angle as the independent variable. These fitted functions produce continuous estimates of clear‐sky total, diffuse, and direct component shortwave irradiances. While this method ignores diurnal changes in such variables as column water vapor and aerosol amounts and changes between clear‐sky days, it is shown that the resultant clear‐sky irradiance estimates have RMS uncertainty comparable to the uncertainty of the measuring instruments themselves. The estimated clear‐sky irradiances are used to estimate the effect of clouds on the downwelling shortwave irradiance as a difference between the measured and clear‐sky amounts. We show that the cloud effect calculations from this method appear to decrease the uncertainty due to systematic pyranometer offsets and cosine response errors. Thus any data set that includes downwelling diffuse and total shortwave measurements can be processed to identify clear‐sky periods and produce estimates of clear‐sky irradiance and cloud effects.

Comparisons between buoy‐observed, satellite‐derived, and modeled surface shortwave flux over the subtropical North Atlantic during the Subduction Experiment

Two years of surface shortwave flux data, from five buoys in the subtropical North Atlantic Ocean during the Subduction Experiment, were used to examine shortwave absorption in the atmosphere, and its partitioning between the clear and cloudy sky. Robust methods were used to isolate the clear‐sky shortwave observations so that they could be directly compared to values derived using a single‐column version of the National Center for Atmospheric Research Community Climate Model radiation code. The model‐derived values agreed with the observations to within 0.5% mean relative error. Additional analysis showed that the model‐data clear‐sky surface shortwave differences showed no systematic relationship with respect to column water vapor amount. These results indicate that clear‐sky absorption of shortwave radiation appears to be well modeled by current theory. Model‐derived clear‐sky surface shortwave values were combined with the observed (all‐sky) values to determine the surface shortwave cloud forcing. The mean of these series were combined with 5‐year mean Earth Radiation Budget Experiment derived top of the atmosphere (TOA) cloud forcing values to estimate the surface to TOA cloud forcing ratio. The resulting values range between 1.25 and 1.59. These values, along with the agreement between modeled and observed clear‐sky surface shortwave, support the suggestion that our current theoretical radiative transfer models do not properly account for the amount of shortwave energy absorbed by the cloudy atmosphere. Mean values from the 2‐year shortwave flux time series were compared to mean values from two climatologies derived from bulk parameterizations that utilize ship‐based cloud reports. These comparisons show that the Oberhuber climatology underestimates the surface shortwave flux by ∼20% (∼40 W m−2), while the Esbensen and Kushnir climatology underestimates the flux by ∼4% (∼8 W m−2). The observed mean values were also compared to five satellite‐derived climatologies. These comparisons showed much better and more consistent agreement, with relative bias errors ranging from about −1 to 6%. Comparisons to contemporaneous, daily‐average satellite derived values show relatively good agreement as well, with relative biases of the order of 2% (∼3–9 W m−2) and root‐mean‐square differences of ∼10% (25–30 W m−2). Aspects of the role aerosols play in the above results are discussed along with the implications of the above results on the integrity of open‐ocean buoy measurements of surface shortwave flux and the possibility of using the techniques developed in this study to remotely monitor the operating condition of buoy‐based shortwave radiometers.

Evaluation of seawater permittivity models at 150 GHz using satellite observations

Multisensor satellite measurements from the special sensor microwave water vapor sounder (SSM/T-2), special sensor microwave imager (SSM/I), and operational linescan system (OLS) are used to retrieve sea surface emissivity at 150 GHz with the goal of assessing selected permittivity models. Retrievals were limited to clear-sky SSM/T-2 field-of-views under arctic winter conditions to minimize atmospheric attenuation. Results show there is sometimes wide disagreement between the models and the observations even when considering measurement and model errors with differences as high as 2.4% (or equivalently /spl ap/5 K). Models consisting of two relaxation time constants rather than one are found to be in closer agreement with the observations. The largest permittivity model errors have substantial effects on physically based atmospheric parameter retrievals with systematic errors of -65% and -20% for a column water vapor amount of 2 kg m/sup -2/ and cloud liquid water path of 0.1 kg m/sup -2/ respectively.