Diagnosis of multilayer clouds using photon path length distributions (original) (raw)
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Journal of Applied Meteorology, 2000
The U.S. Department of Energy's Atmospheric Radiation Measurement (ARM) Program is deploying sensitive, millimeter-wave cloud radars at its Cloud and Radiation Test Bed (CART) sites in Oklahoma, Alaska, and the tropical western Pacific Ocean. The radars complement optical devices, including a Belfort or Vaisala laser ceilometer and a micropulse lidar, in providing a comprehensive source of information on the vertical distribution of hydrometeors overhead at the sites. An algorithm is described that combines data from these active remote sensors to produce an objective determination of hydrometeor height distributions and estimates of their radar reflectivities, vertical velocities, and Doppler spectral widths, which are optimized for accuracy. These data provide fundamental information for retrieving cloud microphysical properties and assessing the radiative effects of clouds on climate. The algorithm is applied to nine months of data from the CART site in Oklahoma for initial evaluation. Much of the algorithm's calculations deal with merging and optimizing data from the radar's four sequential operating modes, which have differing advantages and limitations, including problems resulting from range sidelobes, range aliasing, and coherent averaging. Two of the modes use advanced phase-coded pulse compression techniques to yield approximately 10 and 15 dB more sensitivity than is available from the two conventional pulse modes. Comparison of cloud-base heights from the Belfort ceilometer and the micropulse lidar confirms small biases found in earlier studies, but recent information about the ceilometer brings the agreement to within 20-30 m. Merged data of the radar's modes were found to miss approximately 5.9% of the clouds detected by the laser systems. Using data from only the radar's two less-sensitive conventional pulse modes would increase the missed detections to 22%-34%. A significant remaining problem is that the radar's lower-altitude data are often contaminated with echoes from nonhydrometeor targets, such as insects.
Joint statistics of photon pathlength and cloud optical depth
Geophysical Research Letters, 1999
A mean pressure-and temperature-weighted photon pathlength in the atmosphere can be inferred from moderate resolution measurements in the O 2 A-band. We show a pathlength retrieval method and calibration results for measurements from a Rotating Shadowband Spectroradiometer (RSS), and present the joint statistics of pathlength and cloud optical depth for cloudy skies observed at the Southern Great Plains (SGP) site from September 30 to December 22, 1997. Two different population branches are apparent in the scattergram of the pathlength versus cloud optical depth; we attribute these to 1) singlelayer cases exhibiting small variations of pathlength enhancement over large optical depth ranges; and 2) multiple layer cases with large variances of enhanced photon pathlengths.
Journal of Geophysical Research: Atmospheres, 2011
A Thin-Cloud Rotating Shadowband Radiometer (TCRSR) was developed and deployed in a field test at the Atmospheric Radiation Measurement Climate Research Facility's Southern Great Plains site. The TCRSR measures the forward-scattering lobe of the direct solar beam (i.e., the solar aureole) through an optically thin cloud (optical depth < 8). We applied the retrieval algorithm of Min and Duan (2005) to the TCRSR measurements of the solar aureole to derive simultaneously the cloud optical depth (COD) and cloud drop effective radius (DER), subsequently inferring the cloud liquid-water path (LWP). After careful calibration and preprocessing, our results indicate that the TCRSR is able to retrieve simultaneously these three properties for optically thin water clouds. Colocated instruments, such as the MultiFilter Rotating Shadowband Radiometer (MFRSR), atmospheric emitted radiance interferometer (AERI), and Microwave Radiometer (MWR), are used to evaluate our retrieval results. The relative difference between retrieved CODs from the TCRSR and those from the MFRSR is less than 5%. The distribution of retrieved LWPs from the TCRSR is similar to those from the MWR and AERI. The differences between the TCRSR-based retrieved DERs and those from the AERI are apparent in some time periods, and the uncertainties of the DER retrievals are discussed in detail in this article.
A Study of Cloud Fraction as a Function of Optical Depth Using University of Wisconsin Lidar Data
Thin cirrus clouds have interested and concerned scientists for decades due to the large global coverage and the incomplete understanding of the total radiative properties. Optical depth and height determination of these thin clouds has proven troublesome for scientists using satellite-derived data (such as MODIS) due to the small amount of reflected solar radiation. Studies have shown that when thin cirrus is present, satellites often classify these clouds as lower level clouds or render the clouds undetectable (Wylie 1989). Although the field of view is much smaller than the global coverage of a weather satellite, active remote sensing instruments such as lidar are excellent candidates for retrieving accurate optical depth and height information due to the superior vertical resolution and the ability to detect thin cirrus not detectable by satellites. By using various optical depth thresholds, the amount of cloud cover can be determined for an optical depth lower limit such as 1.0, 0.5, or 0.1. This information can then be used to determine the relative cloud fraction that an instrument may not be detecting. The purpose of this study is to calculate the cloud fraction as a function of various optical depth thresholds using data taken from the University of Wisconsin Arctic High Spectral Resolution Lidar. Results from this study show that over the course of a year when lidar data was gathered at the University of Wisconsin-Madison, there was a 15% increase in the amount of detected cloud cover between an optical depth lower threshold of 2 and that of 0.05. Also found in this study is that for a given cloud fraction, various optical depth limits show as much as a three kilometer difference in altitude when viewing the clouds from above. This study of cloud classification based on optical depth thresholds has shown promising results for future work. Research of this type is useful for scientists calculating the lower optical depth limit of an instrument in orbit as well as validating the percentage of cloud fraction the satellite can view over a given month or season.
Detection of multi-layer and vertically-extended clouds using A-train sensors
Atmospheric Measurement Techniques, 2010
The detection of multiple cloud layers using satellite observations is important for retrieval algorithms as well as climate applications. In this paper, we describe a relatively simple algorithm to detect multiple cloud layers and distinguish them from verticallyextended clouds. The algorithm can be applied to coincident passive sensors that derive both cloud-top pressure from the thermal infrared observations and an estimate of solar photon pathlength from UV, visible, or near-IR measurements. Here, we use data from the A-train afternoon constellation of satellites: cloud-top pressure, cloud optical thickness, and the multi-layer flag from the Aqua MODerate-resolution Imaging Spectroradiometer (MODIS) and the optical centroid cloud pressure from the Aura Ozone Monitoring Instrument (OMI). The cloud classification algorithms applied with different passive sensor configurations compare well with each other as well as with data from the A-train CloudSat radar. We compute monthly mean fractions of pixels containing multi-layer and verticallyextended clouds for January and July 2007 at the OMI spatial resolution (12 km×24 km at nadir) and at the 5 km×5 km MODIS resolution for infrared cloud retrievals. There are seasonal variations in the spatial distribution of the different cloud types. The fraction of pixels containing distinct multi-layer cloud is a strong function of the pixel size. Globally averaged, these fractions are approximately 20% and 5% for OMI and MODIS, respectively. These fractions may be significantly higher or lower depending upon location. There is a much smaller resolution dependence for fractions of pixels containing vertically-extended clouds (∼20% for OMI and slightly less for MODIS globally), suggesting larger spatial scales for these clouds. We also find significantly higher fractions of vertically-extended clouds over land as compared with ocean, particularly in the tropics and summer hemisphere.
Journal of Geophysical Research, 2003
In order to test the strengths and limitations of cloud boundary retrievals from radiosonde profiles, four years of radar, lidar and ceilometer data collected at the ARM SGP site from November 1996 through October 2000 are used to assess the retrievals of WR95) and CE96). The lidar and ceilometer data yield lowest-level cloud base heights that are, on average, within approximately 125m of each other when both systems detect a cloud. These quantities are used to assess the accuracy of coincident cloud base heights obtained from radar and the two radiosonde-based methods applied to 200 m resolution profiles obtained at the same site. The lidar/ceilometer and radar cloud base heights agree by 0.156±0.423 km for 85.27% of the observations, while the agreement between the lidar/ceilometer and radiosonde-derived heights is at best -0.044±0.559 km for 74.60% of all cases. Agreement between radar-and radiosonde-derived cloud boundaries is better for cloud base height than for cloud top height, being at best 0.018±0.641 km for 70.91% of the cloud base heights and 0.348±0.729 km for 68.27% of the cloud top heights. The disagreements between radar-and radiosonde-derived boundaries are mainly caused by broken cloud situations when it is difficult to verify that drifting radiosondes and fixed active sensors are observing the same clouds. In the case of the radar the presence of clutter (e.g., vegetal particles or insects) can affect the measurements from the surface up to approximately 3-5 km, preventing comparisons with radiosonde-derived boundaries. Overall, tend to classify moist layers that are not clouds as clouds and both radiosonde techniques report high cloud top heights that are higher than the corresponding heights from radar. of the highest clouds, which is a distinct possibility, leads to underestimation of cloud top height for these cases. In fact, for all locations where small cloud particles are sampled by the radar, but not the lidar/ceilometer pair, the active sensor instruments may fail to detect them, especially as the distance between the radar and these small particles increases.
Cloud Optical Depth Retrievals From Solar Background "Signals" of Micropulse Lidars
IEEE Geoscience and Remote Sensing Letters, 2007
Pulsed lidars are commonly used to retrieve vertical distributions of cloud and aerosol layers. It is widely believed that lidar cloud retrievals (other than cloud base altitude) are limited to optically thin clouds. Here, we demonstrate that lidars can retrieve optical depths of thick clouds using solar background light as a signal, rather than (as now) merely a noise to be subtracted. Validations against other instruments show that retrieved cloud optical depths agree within 10%-15% for overcast stratus and broken clouds. In fact, for broken cloud situations, one can retrieve not only the aerosol properties in clear-sky periods using lidar signals, but also the optical depth of thick clouds in cloudy periods using solar background signals. This indicates that, in general, it may be possible to retrieve both aerosol and cloud properties using a single lidar. Thus, lidar observations have great untapped potential to study interactions between clouds and aerosols.
Cloud properties derived from two lidars over the ARM SGP site
Geophysical Research Letters, 2011
Active remote sensors such as lidars or radars can be used with other data to quantify the cloud properties at regional scale and at global scale. Relative to radar, lidar remote sensing is sensitive to very thin and high clouds but has a significant limitation due to signal attenuation in the ability to precisely quantify the properties of clouds with a cloud optical thickness larger than 3. The cloud properties for all levels of clouds are derived and distributions of cloud base height (CBH), top height (CTH), physical cloud thickness (CT), and optical thickness (COT) from local statistics are compared. The goal of this study is (1) to establish a climatology of macrophysical and optical properties for all levels of clouds observed over the ARM SGP site and (2) to estimate the discrepancies between the two remote sensing systems (pulse energy, sampling, resolution, etc.). Our first results tend to show that the MPL, which are the primary ARM lidars, have a distinctly limited range within which all of these cloud properties are detectable, especially cloud top and cloud thickness, but this can include cloud base particularly during summer daytime period. According to the comparisons between RL and MPL, almost 50% of situations show a signal to noise ratio too low (smaller than 3) for the MPL in order to detect clouds higher than 7km during daytime period in summer. Consequently, the MPL-derived annual cycle of cirrus cloud base (top) altitude is biased low, especially for daylight periods, compared with those derived from the RL data, which detects cloud base ranging from 7.5 km in winter to 9.5 km in summer (and tops ranging from 8.6 to 10.5 km). The optically thickest cirrus clouds (COT > 0.3) reach 50% of the total population for the Raman lidar and only 20% for the Micropulse lidar due to the difference of pulse energy and the effect of solar irradiance contamination. A complementary study using the cloud fraction derived from the Micropulse lidar for clouds below 5 km and from the Raman lidar for cloud above 5 km allows for better estimation of the total cloud fraction between the ground and the top of the atmosphere. This study presents the diurnal cycle of cloud fraction for each season in comparisons with Long et al.'s (2006) cloud fraction calculation derived from radiative flux analysis.