Evaluation of MODIS water vapour products over China using radiosonde data

Radiosonde data collected from 83 stations in China from January to December 2012 were used to evaluate Moderate Resolution Imaging Spectroradiometer (MODIS) near-infrared (NIR) and thermal infrared (IR) total precipitable water vapour (PWV) products. The results indicate that MODIS NIR PWV products shows better agreement with radiosonde data than with IR PWV products, with the correlation coefficients up to 0.95. The root mean square errors (RMSEs) of NIR PWV range from 2 to 8 mm with different stations, which shows significant regional differences over China. The mean RMSE is about 5.03 mm (~35%) with a positive deviation of 2.56 mm (~18%), indicating the occurrence of a slight overestimation. Moreover, MODIS IR PWV during night-time has a better agreement with radiosonde PWV than that during daytime. The mean RMSE of IR PWV during daytime was ~6.02 mm (~42%), with a positive deviation of 1.54 mm (~11%). The mean RMSE of IR PWV during night-time was ~5.81 mm (~40%), with a negative deviation of approximately ?0.04 mm (~0.25%). Both the NIR and IR PWV products during daytime tend to be higher than radiosonde PWV.     

Measurement of atmospheric water vapour content over a tropical location by dual frequency microwave radiometry

Atmospheric water vapour was measured over a tropical location, Calcutta, by deploying dual frequency microwave radiometers operating at 22.235 and 31.4 GHz. Simultaneous measurements of brightness temperature and hence attenuation in the presence of thin cloud were made for the purpose. An algorithm was developed to exclude the effect of non-precipitating liquid water during the measurement of water vapour. The experimental results are supported by the corresponding radiosonde data analysis. Dual frequency radiometric measurements are shown to improve the rms accuracy, over the single frequency inversion technique, for the measurement of water vapour.     

Satellite remote sensing of atmospheric water vapour

Abstract

The Advanced Very High Resolution Radiometer (AVHRR-2) has two channels in the 10-13/mi window region. These channels are used for remote sensing of the sea surface temperature corrected for atmospheric absorption. The brightness temperature difference between the channels can be directly related to the atmospheric absorption due to water vapour. The problem of water-vapour retrieval from satellite data is examined in detail. The best evaluation of the water-vapour content is obtained from the spectrometric data of the Infrared Interferometer Spectrometer (IRIS), with an error of about + 3kg/m². It is, however, feasible to obtain the water-vapour content from AVHRR data with an algorithm derived from radiative transfer model simulations. The retrieved water vapour has an error of ±5kg/m² when compared with ship data. It is possible to use the remotely sensed water vapour for the inference of the boundary-layer structure. The information is, however, limited for water vapour contained near the surface.     

Integrated water vapour content by scanning radiometry

Abstract

Radiometry at the water vapour line with a scanning antenna beam from horizon to horizon through the zenith, in the vertical plane, was shown to be useful in estimating the integrated water vapour content from the zenith angle variation of radiometric temperature. The beam scanning may be made at a rate slower than the radiometer time constant using a Dicke type radiometer. Alternatively, a fast scanning beam may be used in a total power radiometer with a synchronous detection of the radiometer output at the scanning frequency to obtain a space domain Dicke switching for the measurements. A simultaneous scanning beam with 22.235 and 31.4GHz radiometers would allow us to separate the liquid water, if any, from the integrated water vapour content. Theoretical studies of the scanning beam radiometer performance in the estimation of integrated water vapour content from radiosonde data is presented and some typical results of Dicke type scanning beam radiometers at 22.235 and 31.4 GHz are also presented in this paper.     

Ground-based single-frequency microwave radiometric measurement of water vapour

The integrated water vapour content of the atmosphere is measured by a ground-based radiometer at 22.234 GHz at the Instituto Nacional de Pesquisas Espaciais (INPE), Brazil. The data set has been partitioned into two sets: one for no cloud and the other for cloudy conditions. The attenuation (dB) under the no cloud condition was always found to vary within 1.0–1.5 dB, except at around 1400 hrs (local time) through 1800 hrs (local time), and it reached a value of more than 1.5 dB with a maximum at 1700. This is in conformity with the theoretically calculated values of water vapour density over the same location. An effort has been made to get the regression relation between the measured and calculated water vapour content, for which the RMS error was found to be 0.49 kg m^?2.     

Inter annual,spatial, seasonal,and diurnal variability of precipitable water vapour over northeast India using GPS time series

We present multi-scale variability of GPS-derived column integrated precipitable water vapour (PWV) estimated over five continuous GPS sites of northeast India from 2004 to 2012. PWV is estimated from GPS-derived zenith total delay using observed surface pressure and temperature from collocated meteorological sensors as well as obtained by interpolating European Centre for Medium-Range Weather Forecasts (ECMWF) reanalysis project (ERA-Interim) global reanalysis dataset. PWV estimated using ERA-Interim-derived parameters compare well with the PWV estimated using observed meteorological parameters with bias of less than ± 0.1 mm and highest root mean square error of 0.56 mm. The average PWV for the study period is about 17 mm at Bomdila in the Eastern Himalayas, about 20 mm at Shillong in Shillong plateau, about 31 mm at Lumami in Arokan-Yoma Hill ranges, and about 43 mm at Guwahati and Tezpur in Assam valley. The high altitude sites show low annual PWV variability (around 49%) than the low altitude sites (around 63–67%). Seasonal PWV value coincides with the monsoon with maximum in summer and minimum in the winter. However, percentage seasonal PWV variability is found to be almost same (around 68%) for all the five sites. The Assam valley sites do not show a distinct diurnal cycle whereas the high altitude sites indicate a distinct diurnal cycle coinciding with the daily solar cycle. Insights in to GPS PWV variability and rainfall are presented for the study period.     

Radiometric Transfer: Example‐based Radiometric Linearization of Photographs

We present an example‐based approach for radiometrically linearizing photographs that takes as input a radiometrically linear exemplar image and a target regular uncalibrated image of the same scene, possibly from a different viewpoint and/or under different lighting. The output of our method is a radiometrically linearized version of the target image. Modeling the change in appearance of a small image patch seen from a different viewpoint and/or under different lighting as a linear 1D subspace, allows us to recast radiometric transfer in a form similar to classic radiometric calibration from exposure stacks. The resulting radiometric transfer method is lightweight and easy to implement. We demonstrate the accuracy and validity of our method on a variety of scenes.     

Radiometric measurements over bare and vegetated fields at 1.4-GHz and 5-GHz frequencies

Results of radiometric measurements over bare and vegetated fields with dual-polarized microwave radiometers at 1.4-GHz and 5-GHz frequencies are presented. The measured brightness temperatures over bare fields are shown to compare favorably with those calculated from radiative transfer theory with two constant parameters characterizing surface roughness effect. The presence of vegetation cover is found to reduce the sensitivity to soil moisture variation. This sensitivity reduction is generally more pronounced the denser the vegetation cover and the higher the frequency of observation. The effect of vegetation cover is also examined with respect to the measured polarization factor at both frequencies. With the exception of dry corn fields, the measured polarization factor over vegetated fields is found appreciably reduced compared to that over bare fields. A much larger reduction in this factor is found at 5 GHz than at 1.4 GHz.     

Sensitivity of near infrared total water vapour estimate to calibration errors

Analysis of satellite data to estimate the precipitable water (also called the columnar water vapour) amount often leads to systematic errors in deduced precipitable water (PW). The causes of systematic errors are likely to be instrumental calibration errors rather than variability of atmospheric parameters. We use the MODTRAN 4.0 radiative transfer code to model effects of various calibration errors on the Multi-spectral Thermal Imager (MTI) daytime total water vapour estimate. From the considered sources of calibration errors (spectral band centre error, spectral bandwidth error and radiometric calibration error) the radiometric calibration error has the largest influence on the accuracy of total water vapour estimate. When the radiometric calibration error between 1% and 5% is combined with the estimated spectral band centre error of 1 nm and the bandwidth error of 0.5 nm, the total systematic error of the columnar water vapour estimate is expected to be between 8% and 26%. The accuracy of the retrieved PW using the MTI imagery over the NASA Stennis site and Oklahoma DOE (Department of Energy) ARM (Atmospheric Radiation Measurement program) site is about 17%, well within the estimated range due to calibration errors. A similarity between the MTI and the MODIS (Moderate Resolution Imaging Spectral-Radiometer) bands used for water vapour estimate suggests that a similar error analysis may be valid for the MODIS sensor. However, the narrow band instruments (with bandwidth around 10 nm) are much more sensitive to the band centre calibration error.     

Study of aerosol optical depth,ozone, and precipitable water vapour content over Sinhagad,a high-altitude station in the Western Ghats

This article reports the results of a study related to variations in columnar aerosol optical depth (AOD), total column ozone (TCO), and precipitable water content (PWC) over a high-altitude station, Sinhagad (18° 21′ N, 73° 45′ E, 1450 m above mean sea level (AMSL)), employing a microprocessor-based total ozone portable spectrometer, MICROTOPS-II, comprising both a sun photometer and ozonometer, during November 2009–April 2010. The aerosol optical depth at 500 nm (AOD_{500 nm}) portrayed seasonal variation with higher values (0.39) in summer and lower values (0.15) in winter. The TCO and PWC also exhibited lower values in winter and started increasing by the pre-monsoon season. The Ångström wavelength exponent, α, was found to be high (1.79) during February, indicating the relative dominance of accumulation-mode particles. During the summer season, the lower value (0.94) of the Ångström wavelength exponent indicates the relative dominance of coarse-mode particles. The ground-based observations from MICROTOPS-II revealed good correlation with satellite observations of the Moderate Resolution Imaging Spectroradiometer (MODIS) and Ozone Monitoring Instrument (OMI). The observed short-wave solar flux at the bottom of the atmosphere decreased due to aerosol extinction and was found to be 19 and 78 W m^–2 for the winter and pre-monsoon seasons, respectively. This implies that greater concentrations of accumulation-mode particles – which are due to local anthropogenic sources – affected the down-welling radiation than those from natural sources – which are due to long-range transport processes – over the experimental location.     

Satellite-derived low-level atmospheric water vapour content from synergy of AVHRR with HIRS

Abstract

The infrared sensor systems AVHRR (Advanced Very High Resolution Radiometer) and HIRS (High resolution Infrared Radiation Sounder) on board the NOAA-7 satellite are studied theoretically by means of radiative transfer calculations to enable the development of new retrieval techniques for atmospheric water vapour profiles. Simulations of radiometer signals have been performed for a large set of atmospheres from the middle and tropical latitudes. Subsequent development of a physical-statistical retrieval method demonstrates the usefulness of a coupling of both radiometers for water vapour retrievals in a single HIRS field of view. Total column amounts as well as the amounts in thick layers (150-200 hPa (thick) in the lower troposphere can be derived with an accuracy of 5-15 per cent and 15-25 per cent respectively. The amounts in thinner layers (50hPa) can be estimated with accuracies between 20 and 30 per cent. The AVHRR split window channels are a powerful tool in the water vapour retrievals. The technique developed benefits from the simultaneous retrieval of temperature profiles and surface temperatures. Accounting for scan-angle dependencies explicitly leads to improved retrievals. The synergy of AVHRR with HIRS increases the number of retrievals in partially cloudy fields of view compared with HIRS retrievals alone. A case study demonstrates the capability of the method to resolve water vapour structures with a high spatial resolution and its value in areas where conventional measurements from radiosonde ascents are sparse.     

Radiometric measurements and crop yield forecasting Some observations over millet and sorghum experimental plots in Mali

Abstract

A series of 45 plots of millet and sorghum were installed in Sotuba, Mali, during the 1985 rainy season. They comprised five varietiescommonly found in the surrounding countryside. Radiometric measurements together with phenological observations and leaf measurements were made every 7-10 days. At harvest the biomass was dried and each of its components was weighed.The data analysis shows that the grain-to-straw relationship can vary widely and is less dependent on the species/variety than on the environmental conditions. The comparison of accumulated normalized difference vegetation index (NDVI) versus instantaneous NDVI has shown the superiority of the first one through its higher stability. Total biomass is linearly correlated with accumulated NDVI since the tillering, while the final grain production is better correlated with accumulated NDVI after the booting. Furthermore, the terms of the regression seem to be time dependent.     

Atmospheric correction of ENVISAT/MERIS data over case II waters: the use of black pixel assumption in oxygen and water vapour absorption bands

The Medium Resolution Imaging Spectrometer (MERIS) sensor, with its good physical design, can provide excellent data for water colour monitoring. However, owing to the shortage of shortwave-infrared (SWIR) bands, the traditional near-infrared (NIR)–SWIR algorithm for atmospheric correction in inland turbid case II waters cannot be extended to the MERIS data directly, which limits its applications. In this study, we developed a modified NIR black pixel method for atmospheric correction of MERIS data in inland turbid case II waters. In the new method, two special NIR bands provided by MERIS data, an oxygen absorption band (O₂ A-band, 761 nm) and a water vapour absorption band (vapour A-band, 900 nm), were introduced to keep the assumption of zero water-leaving reflectance valid according to the fact that both atmospheric transmittance and water-leaving reflectance are very small at these two bands. After addressing the aerosol wavelength dependence for the cases of single- and multiple-scattering conditions, we further validated the new method in two case lakes (Lake Dianchi in China and Lake Kasumigaura in Japan) by comparing the results with in situ measurements and other atmospheric correction algorithms, including Self-Contained Atmospheric Parameters Estimation for MERIS data (SCAPE-M) and the Basic ERS (European Remote Sensing Satellite) & ENVISAT (Environmental Satellite) (A)ATSR ((Advanced) Along-Track Scanning Radiometer) and MERIS (BEAM) processor. We found that the proposed method had acceptable accuracy in the bands within 560–754 nm (MERIS bands 5–10) (average absolute deviation (AAD) = 0.0081, average deviation (AD) = 0.0074), which are commonly used in the estimation models of chlorophyll-a (chl-a) concentrations. In addition, the performance of the new method was superior to that of the BEAM processor and only slightly worse than that of SCAPE-M in these bands. Considering its acceptable accuracy and simplicity both in principle and at implementation compared with the SCAPE-M method, the new method provides an option for atmospheric correction of MERIS data in inland turbid case II waters with applications aiming for chl-a estimation.     

Estimation of precipitable water vapour from NOAA-AVHRR data during the Hapex Sahel experiment

The simple split-window and the transmittance ratio method for estimation of atmospheric precipitable water content were analysed using data gathered through the Hapex Sahel Experiment conducted in Niger from August to October 1992. Results are presented for the Hapex Sahel square degree. Both methods are designed to work with thermal infrared data from the Advanced Very High Resolution Radiometer flown on the NOAA polar orbiting satellites. The performance of the methods were evaluated with radiosonde measurements and prognosticated water vapour fields obtained from the global circulation model used at the European Centre for Medium Weather Forecast. The implications of extending the simple split-window method to work over land surfaces were investigated by use of both simulated and observed data. It was shown that changing surface conditions, i.e., emissivity and surface temperature, could significantly influence the split-window estimate of water vapour content and therefore inhibit the implementation of a global model. An optimized split-window algorithm with inclusion of the land surface temperature could increase the explained variance of the radiosonde measurements and it could reproduce the variation of the water vapour through the growing season very well. The performance of the transmittance ratio method was shown to be more unstable but in some cases it compared well with observed data. This result might be attributed both to the non-optimal characteristic of the satellite sensor as well as the problems with thermal homogeneity of the land surface.     

多模态CCD相机辐射校正方法研究

多模态CCD相机可以根据地物辐亮度的差异,调节相机的积分时间和增益以获取理想的影像数据。如何对不同积分时间和增益设置下获取的数据进行辐射校正,是数据定量化运用的关键。为了因避免相机设置改变而重复辐射定标,本文通过实验发现相机暗电流受积分时间和增益的影响较小,在此基础上本文提出一种辐射校正方法可以基于某种积分时间和增益设置下的辐射校正函数推算其它设置下的辐射校正函数。实验结果显示此方法较为理想。     

Simulation of satellite water vapour lidar measurements: Performance assessment under real atmospheric conditions

A lidar simulator has been applied to assess the performances of a satellite water vapour differential absorption lidar (DIAL) system. Measurements performed by the airborne Deutsches Zentrum für Luft-und Raumfahrt (DLR) water vapour DIAL on 15 May 2002 during ESA's Water Vapour Lidar Experiment (WALEX), in combination with PSU/NCAR Mesoscale Model (MM5) output, were used to obtain backscatter and water vapour fields with high resolution and accuracy. These data and model output serve as input for the simulator, allowing for the performance of satellite DIAL under highly-inhomogeneous atmospheric conditions including clouds to be assessed. The airborne measurements show an intrusion of stratospheric air into the troposphere, and MM5 data used above the DLR Falcon airplane flight altitude are characterized by very high upper tropospheric humidity levels, comparable to those associated with strong mid-latitude transport events from the troposphere to the lowermost stratosphere. Results of the simulator reveal that the maximum systematic error does not exceed 5% up to 16 km, except in the presence of thick cirrus and mid level clouds with an optical thickness up to 2 and, occasionally, inside the dry stratospheric intrusion, while the random error is less than 20% up to 16 km when spatial measurement resolutions are applied that follow the World Meteorological Organization (WMO) threshold observational requirements for numerical weather prediction (NWP). The bias is even smaller if a drier upper troposphere/lower stratosphere (UTLS) region from a reference atmosphere is considered. The results confirm the capability of satellite water vapour DIAL systems to retrieve thin structures of the tropospheric water vapour and particle backscatter fields, as well as its capability to provide low bias and random error measurements even in the presence of clouds.     

Sodar observation of elevated layers around Calcutta

Abstract

Sodar (sound radar) echograms recorded at the Indian Statistical Institute, Calcutta, often exhibit stratified or elevated layers over the ground-based inversion. These have been noticed often after the occurrences of thunderstorms and rain. These layers may be single or multiple, continuous or intermittent and are associated with some perturbations even as gravity wavelike structures. All these features of the lower atmospheric structures over this coastal station, as detected by a sound radar, are discussed in this paper.     

Improvements in land surface temperature retrieval based on atmospheric water vapour content and atmospheric temperature

Three methods are currently used to retrieve land surface temperatures (LSTs) from thermal infrared data supplied by the Thematic Mapper (TM) and Enhanced Thematic Mapper Plus (ETM+) sensors: the radiative transfer equation, mono-window, and generalized single-channel algorithms. Most retrieval results obtained using these three methods have an average error of more than 1 K. But if the regional mean atmospheric water vapour content and temperature are supplied by in situ radiosounding observations, the mono-window algorithm is able to provide better results, with a mean error of 0.5 K. However, there are no in situ radiosounding data for most regions. This article provides an improved method to retrieve LST from Landsat TM and ETM+ data using atmospheric water vapour content and atmospheric temperature, which can be obtained from remote-sensing data. The atmospheric water vapour content at the pixel scale was first calculated from Moderate Resolution Imaging Spectroradiometer (MODIS) data. The emissivities of various land covers and uses were then defined by Landsat TM or ETM+ data. In addition, the temperature–vegetation index method was applied to map area-wide instantaneous near-surface air temperatures. The parameters of mean atmospheric water vapour content and temperature and land surface emissivity were finally inputted to the mono-window algorithm to improve the LST retrieval precision. Our results indicate that this improved mono-window algorithm gave a significantly better retrieval of the estimated LST than that using the standard mono-window algorithm, not only in dry and elevated mountain regions but also in humid regions, as shown by the bias, standard deviation (σ), and root mean square deviation (RMSD). In Madoi County, the improved mono-window algorithm validated against the LST values measured in situ produced a bias and RMSD of –0.63 K and 0.91 K, respectively, compared with the mono-window algorithm’s bias and RMSD of –1.08 K and 1.27 K. Validated against the radiance-based method, the improved algorithm shows bias and RMSD values of –1.08 K and 1.27 K, respectively, compared with the initial algorithm’s bias and RMSD –1.65 K and 1.75 K. Additionally, the improved mono-window algorithm also appeared to be more accurate than the mono-window algorithm, with lower error values when validated against in situ measurement and the radiance-based method in the validation area in Zhangye City, Gansu Province, China. Remarkable LST accuracy improvements are shown by the improved mono-window algorithm, with better agreement not only with the in situ measurements but also with the simulated LSTs in the two validation areas, indicating the soundness and suitability of this method.