M. M. Khaiyer

Thin Liquid Water Clouds: Their Importance and Our Challenge

Abstract Many of the clouds important to the Earths energy balance, from the Tropics to the Arctic, contain small amounts of liquid water. Longwave and shortwave radiative fluxes are very sensitive to small perturbations of the cloud liquid water path (LWP), when the LWP is small (i.e., < 100 g m−2; clouds with LWP less than this threshold will be referred to as “thin”). Thus, the radiative properties of these thin liquid water clouds must be well understood to capture them correctly in climate models. We review the importance of these thin clouds to the Earths energy balance, and explain the difficulties in observing them. In particular, because these clouds are thin, potentially mixed phase, and often broken (i.e., have large 3D variability), it is challenging to retrieve their microphysical properties accurately. We describe a retrieval algorithm intercomparison that was conducted to evaluate the issues involved. The intercomparison used data collected at the Atmospheric Radiation Measurement (ARM) S...

Aerosol and Cloud Microphysical Characteristics of Rifts and Gradients in Maritime Stratocumulus Clouds

A cloud rift is characterized as a large-scale, persistent area of broken, low-reflectivity stratocumulus clouds usually surrounded by a solid deck of stratocumulus. A rift observed off the coast of California was investigated using an instrumented aircraft to compare the aerosol, cloud microphysical, and thermodynamic properties in the rift with those of the surrounding solid stratocumulus deck. The microphysical characteristics in the solid stratocumulus deck differ substantially from those of a broken, cellular rift where cloud droplet concentrations are a factor of 2 lower than those in the solid cloud. Furthermore, cloud condensation nuclei (CCN) concentrations were found to be about 3 times greater in the solid-cloud area compared with those in the rift. Although drizzle was observed near cloud top in parts of the solid stratocumulus cloud, the largest drizzle rates were associated with the broken clouds within the rift area and with extremely large effective droplet sizes retrieved from satellite data. Minimal thermodynamic differences between the rift and solid cloud deck were observed. In addition to marked differences in particle concentrations, evidence of a mesoscale circulation near the solid cloud–rift boundary is presented. This mesoscale circulation may provide a mechanism for maintaining a rift, but further study is required to understand the initiation of a rift and the conditions that may cause it to fill. A review of results from previous studies indicates similar microphysical characteristics in rift features sampled serendipitously. These observations indicate that cloud rifts are depleted of aerosols through the cleansing associated with drizzle and are a manifestation of natural processes occurring in marine stratocumulus.

Near-real time cloud retrievals from operational and research meteorological satellites

A set of cloud retrieval algorithms developed for CERES and applied to MODIS data have been adapted to analyze other satellite imager data in near-real time. The cloud products, including single-layer cloud amount, top and base height, optical depth, phase, effective particle size, and liquid and ice water paths, are being retrieved from GOES- 10/11/12, MTSAT-1R, FY-2C, and Meteosat imager data as well as from MODIS. A comprehensive system to normalize the calibrations to MODIS has been implemented to maximize consistency in the products across platforms. Estimates of surface and top-of-atmosphere broadband radiative fluxes are also provided. Multilayered cloud properties are retrieved from GOES-12, Meteosat, and MODIS data. Native pixel resolution analyses are performed over selected domains, while reduced sampling is used for full-disk retrievals. Tools have been developed for matching the pixel-level results with instrumented surface sites and active sensor satellites. The calibrations, methods, examples of the products, and comparisons with the ICESat GLAS lidar are discussed. These products are currently being used for aircraft icing diagnoses, numerical weather modeling assimilation, and atmospheric radiation research and have potential for use in many other applications.

Anisotropy of Land Surface Skin Temperature Derived from Satellite Data

The land skin temperature, an important feature for agricultural monitoring, convective processes, and the earth’s radiation budget, is monitored from limited-view satellite imagers. The angular dependence of this parameter is examined using simultaneous views of clear areas from up to three geostationary satellites. Daytime temperatures from different satellites differed by up t o 6 K and varied as a function of the time of day. Larger differences are expected to occur but were not measured because of limited viewing angles. These differences suggest that biases may occur in both the magnitude and phase of the diurnal cycle of skin temperature and its mean value whenever geostationary satellite data are used to determine skin temperature. The temperature differences were found over both flat and mountainous regions with some slight dependence on vegetation. The timing and magnitude of the temperature differences provide some initial validation for relatively complex model calculations of skin temperature variability. The temperature differences are strongly correlated with terrain and the anisotropy of reflected solar radiation for typical land surfaces. These strong dependencies suggest the possibility for the development of a simple empirical approach for characterizing the temperature anisotropy. Additional research using a much greater range of viewing angles is required to confirm the potential of the suggested empirical approach.

Evaluation of the NASA GISS Single-Column Model simulated clouds using combined surface and satellite observations.

Three years of surface and Geostationary Operational Environmental Satellite (GOES) data from the Department of Energy Atmospheric Radiation Measurement (ARM) Southern Great Plains (SGP) site are used to evaluate the NASA GISS Single Column Model (SCM) simulated clouds from January 1999 to December 2001. The GOES-derived total cloud fractions for both 0.58 and 2.58 grid boxes are in excellent agreement with surface observations, suggesting that ARM point observations can represent large areal observations. Low (,2 km), middle (2‐6 km), and high (.6 km) levels of cloud fractions, however, have negative biases as compared to the ARM results due to multilayer cloud scenes that can either mask lower cloud layers or cause misidentifications of cloud tops. Compared to the ARM observations, the SCM simulated most midlevel clouds, overestimated low clouds (4%), and underestimated total and high clouds by 7% and 15%, respectively. To examine the dependence of the modeled high and low clouds on the large-scale synopticpatterns,variablessuch as relative humidity(RH) andverticalpressurevelocity(omega)fromNorth American Regional Reanalysis (NARR) data are included. The successfully modeled and missed high clouds are primarily associated with a trough and ridge upstream of the ARM SGP, respectively. The PDFs of observed high and low occurrence as a function of RH reveal that high clouds have a Gaussian-like distribution with mode RH values of ;40%‐50%, whereas low clouds have a gammalike distribution with the highest cloud probability occurring at RH ;75%‐85%. The PDFs of modeled low clouds are similar to those observed;however, forhighcloudsthePDFsareshiftedtowardhighervaluesofRH.Thisresultsin anegative bias for the modeled high clouds because many of the observed clouds occur at RH values below the SCMspecified stratiformparameterization thresholdRH of 60%.Despite manysimilaritiesbetweenPDFs derived from the NARR and ARM forcing datasets for RH and omega, differences do exist. This warrants further investigation of the forcing and reanalysis datasets.

NASA-Langley web-based operational real-time cloud retrieval products from geostationary satellites

At NASA Langley Research Center (LaRC), radiances from multiple satellites are analyzed in near real-time to produce cloud products over many regions on the globe. These data are valuable for many applications such as diagnosing aircraft icing conditions and model validation and assimilation. This paper presents an overview of the multiple products available, summarizes the content of the online database, and details web-based satellite browsers and tools to access satellite imagery and products.

Near-Real-Time Satellite Cloud Products for Icing Detection and Aviation Weather over the USA

ABSTRACT A set of physically based retrieval algorithms has beendeveloped to derive from multispectral satellite imagery avariety of cloud properties that can be used to diagnoseicing conditions when upper-level clouds are absent.The algorithms are being applied in near-real time to theGeostationary Operational Environmental Satellite(GOES) data over Florida, the Southern Great Plains, andthe midwestern USA. The products are available in imageand digital formats on the world-wide web. The analysissystem is being upgraded to analyze GOES data overthe CONUS. Validation, 24-hour processing, andoperational issues are discussed. INTRODUCTION Locations of aircraft icing conditions are currentlyavailable to pilots as a result of forecast model predictionsor from pilot reports (PIREPS). These diagnoses aregenerally valuable but leave much room for improvementbecause of forecast errors or because of the age andsparse distribution of PIREPS. It has been recognizedfor many years that GOES data might be used to increasethe spatial coverage and timeliness of icing reportsbecause, the satellite imager provides direct informationon cloud temperature [1] and some indication of cloudphase form the 3.9-µm channel [2]. The interpretation ofthe radiances is complicated by the angular dependenceof the reflected intensities and ambiguity between iceand liquid water in some conditions. Thus, furtheradvancement in satellite icing detection requires a morequantitative analyses of the satellite-observed radiances.With the need for better understanding of the role ofclouds in climate, physically based algorithms have beendeveloped to retrieve cloud microphysical propertiesfrom polar orbiting sat ellites for climate research, inparticular, the NASA Clouds and the Earth’s RadiantEnergy System Project [3]. The same algorithms are alsoapplicable to the GOES imager data and have been usedfor near-real-time analysis over the Atmos phericRadiation Measurement (ARM) southern Great Plains(SGP) domain [4]. Because the retrieved cloud productsinclude the cloud phase, temperature Tc, dropleteffective radius re, and liquid water path LWP, theyshould be valuable for diagnosing icing conditions,which require the presence of supercooled liquid waterSLW, larger droplets, and large liquid water contentLWC. Smith et al. [5,6] showed that the GOES analysisyielded SLW in 98% of the available positive icingPIREPS for a variety of viewing and illuminationconditions, demonstrating the excellent potential for thephysical retrieval approach. Efforts to better quantifyicing conditions from the GOESretrievals are continuing[7]. For effective use by the USA air traffic system, it isnecessary to have near-real time data available over theentire contiguous USA (CONUS). This paper describesthe development of an expansion of the currently limitedGOESprocessing to include the CONUS in parallel withthe efforts to quantitatively relate the retrieved c loudproperties to objective measures of icing.

Detection and retrieval of multi-layered cloud properties using satellite data

Four techniques for detecting multilayered clouds and retrieving the cloud properties using satellite data are explored to help address the need for better quantification of cloud vertical structure. A new technique was developed using multispectral imager data with secondary imager products (infrared brightness temperature differences, BTD). The other methods examined here use atmospheric sounding data (CO2-slicing, CO2), BTD, or microwave data. The CO2 and BTD methods are limited to optically thin cirrus over low clouds, while the MWR methods are limited to ocean areas only. This paper explores the use of the BTD and CO2 methods as applied to Moderate Resolution Imaging Spectroradiometer (MODIS) and Advanced Microwave Scanning Radiometer EOS (AMSR-E) data taken from the Aqua satellite over ocean surfaces. Cloud properties derived from MODIS data for the Clouds and the Earths Radiant Energy System (CERES) Project are used to classify cloud phase and optical properties. The preliminary results focus on a MODIS image taken off the Uruguayan coast. The combined MW visible infrared (MVI) method is assumed to be the reference for detecting multilayered ice-over-water clouds. The BTD and CO2 techniques accurately match the MVI classifications in only 51 and 41% of the cases, respectively. Much additional study is need to determine the uncertainties in the MVI method and to analyze many more overlapped cloud scenes.