A multi-wavelength Raman lidar system which includes both vibrational rotational Raman and Mie scattering spectra
has been designed and described. A retrieval algorithm for water vapor and temperature has also been developed based
on the potential observations from this Raman lidar system. The performance of this retrieval method and the new lidar
system has been evaluated with a synthetic test. Using the U.S. standard atmosphere model and main parameters of this
lidar system, we have obtained signal to noise ratio(SNR)of water-vapor backscatter signals under different
circumstances of aerosol content, pulse emission energy and signal integration time. With the model calculated
backscatter signals, both atmospheric water-vapor and temperature profiles have been retrieved and their uncertainties
have been analyzed. These synthetic tests indicate that our new lidar system can obtain profiles of water-vapor and
temperature at both day and night time, but with different detection heights. The retrieval algorithm shows less than 30%
relative error for water vapor mixing ratio and good accuracy with a minimum detection of temperature less than 2 K.
This paper provides an inter-comparison study of various ground-based cloud retrieval algorithms that have been
developed to obtain cloud water content. The retrieval algorithms are classified into three types, statistical
parameterization algorithm, physical retrieval algorithm, and optimal iteration method. Analyses indicate that physical
retrieval algorithms are theoretically accurate, however, assumptions used in these methods make it challenging for them
to obtain highly reliable results. Empirical parameterization methods are simple and can be easily applied. However,
these methods are generally based on very limited cloud samples for certain types of clouds and locations, they have
much larger uncertainties. In contrast, the optimal iteration method seems to have relatively higher accuracies since the
retrieval results make the forward model simulations match observations. However, the accuracy of optimal iteration
method is highly dependent on the reliability of the forward models and the a priori information.
Surface bi-directional reflectance distribution function (BRDF) and albedo properties are retrieved over the Atmospheric Radiation Measurement (ARM) Program Southern Great Plains (SGP) area. A landcover-based fitting approach is employed by using a newly developed landcover classification map and the MODIS 10-day surface reflectance product (MOD09). The surface albedo derived by this method is validated against other satellite systems (e.g. Landsat-7 and MISR) and ground measurements made by an ASD spectroradiometer. Our results show good agreements between the datasets in general. The advantages of this method include the ability to capture rapid changes in surface properties and an improved performance over other methods under a frequent presence of clouds. Results indicate that the developed landcover-based fitting methodology is valuable for generating spatially and temporally complete surface albedo and BRDF maps using MODIS observations.
This article analyzed annual, seasonal and daily variations of total solar and ultraviolet (UV) radiation, based on observed data over Nanjing area from May 2001 to April 2002. The study shows that the daily variation of solar radiation and UV-radiation is bigger at noon, smaller in the morning and afternoon. When looking at it on an annual scale the variation is bigger in summer, smaller in winter. A linear regression equation has been set up to calculate quantities of UV-radiation that reaches the earth in a sunny day from daily-observed data.
Since the satellites provide frequent and global observations of atmospheric and terrestrial environment, attempts have been made to use satellite data for long-term monitoring of land reflectances, vegetation indices and clouds properties. Although the construction and characteristics of spaceborne instruments may be quite similar, they are not identical among all missions, even for the same type of instrument like AVHRR. Consequently, the effect of varying spectral response may create an artificial noise imposed upon a subtle natural variability. We report the results of a study on the sensitivity of Normalized Difference Vegetation Index (NDVI), surface and cloud reflectance to differences in instrument spectral response functions (SRF) for various satellite sensors. They include AVHRR radiometers onboard NOAA satellites NOAA-6 - NOAA-16, the Moderate Resolution Imaging Spectroradiometer (MODIS), the VEGETATION sensor (VGT) and the Global Imager (GLI). We also analyzed the SRF effects for several geostationary satellites used for cloud studies, such as GOES-8 - 12, METEOSAT-2 - 7, GMS -1 - 5. The results obtained here demonstrate that the effect of instrument spectral response function cannot be ignored in long-term monitoring studies that employ space observations from different sensors. The SRF effect introduces differences in observed reflectances and retrieved quantities that may be comparable or exceed the range of natural variability and possible systematic trends, the contribution from the calibration, atmospheric and other corrections. Some modeling results were validated against real satellite observations with good agreement.
This paper presents a comprehensive investigation of Canadian boreal forest fires using satellite measurements. Algorithms were developed for detecting active fires (hotspots), burned areas, and smoke plumes using single-day NOAA-AVHRR images and 10-day AVHRR NDVI composites. The algorithms were rigorously validated using conventional fire survey data. The hotspot algorithm identified almost all fire events, but cumulative hotspot area was significantly smaller (approximately 30%) than burned area reported by fire agencies. The hybrid, burn mapping technique provided estimates of Canada-wide burned area that were within 5 percent of official statistics. A neural-network classifier was also developed that allows smoke plumes to be effectively separated from cloud cover at a regional scale.
Data from the ScaRaB radiometer flown on board the Meteor-3/7 satellite were first employed for validating and correcting a TOA Earth radiation budget product generated from GOES-7 and the latter was then combined with ground radiation measurements for addressing the effect of clouds on atmospheric absorption of solar radiation. By virtue of comparison between coincident and collocated radiative quantities derived from ScaRaB and GOES sensors, it was found that GOES calibrations for both visible and infrared window channels appear to be adequate, but narrow to broad-band conversion of short-wave measurements suffers systematic errors. After correcting this problem, the cloud radiative forcing at the top of the atmosphere (TOA) and at the surface were derived from space- and ground-based measurements made during the US Atmospheric Radiation Measurement (ARM). The ratio of the two forcing terms is in excellent agreement with that determined by radiative transfer models, in contradiction to the recent claim of cloud absorption anomaly.
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