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A cloud effect, called 'cloud bright spot,' is presented in terms of field measurements, theory, and significance to remote sensing. Field measurements of global solar radiation show that clouds can cause ground bright spots having a 30% higher incident solar radiation flux than surrounding areas. The possible cause and recommendations concerning ground truth measurements and instrumentation are also presented.
During SUCCESS two independent and independently calibrated spectrometers were deployed at a surface site to determine the spectrally resolved downwelling solar irradiance. Preliminary evidence shows that for cloud-free conditions there is general agreement between measurements and model derived irradiance, at least to within instrumental and model uncertainties of 5%, with greatest disagreement Occurring in the 400 nm - 700 nm band. Cirrus effects are most prominent in the near-infrared bands beyond 1500 nm while the influence of very thin (subvisual) cirrus is evident in the scattered flux at the surface. Further remote sensing applications and the assessment of cloud absorption is contingent upon measurements of the surface spectral albedo.
Satellite-based measurements of global ice cloud microphysical properties are sampled to develop a novel set of physical parameterizations, relating to cloud layer temperature and effective diameter D(e), that can be implemented for two separate applications: in numerical weather prediction models and lidar-based cloud radiative forcing studies. Ice cloud optical properties (i.e., spectral scattering and absorption) are estimated based on the effective size and habit mixture of the cloud particles. Historically, the ice cloud D(e) has been parameterized from aircraft in situ measurements. However, aircraft-based parameterizations are opportunistic in that they only represent specific types of clouds (e.g., convective anvil, tropopause-topped cirrus) in the regions in which they were sampled and, in some cases, are limited in fully resolving the entire vertical cloud layer. Breaking away from the aircraft-based parameterization paradigm, this study is the first of its kind to attempt a parameterization of D(e) as a function of temperature, ice water content (IWC), and lidar-derived extinction from satellite-based global oceanic measurements of ice clouds. Data from both active and passive remote sensing sensors from two of NASA’s A-Train satellites, CloudSat and CALIPSO, are collected to guide development of globally robust parameterizations of all ice cloud types and one exclusively for cirrus clouds.
The NASA Moderate Resolution Imaging Spectrometer (MODIS) on the Terra Spacecraft has been collecting scientific data since February of 2000. MODIS is a major facility instrument for remote sensing of the atmosphere, land surfaces, and ocean color. On the MODIS instruments, there are five channels located within and around the .0.94 micron water vapor band absorption region for remote sensing of atmospheric water vapor. There is also a channel located at 1.375 micron for detecting thin cirrus clouds. We will describe the basic principles for using these near-IR channels for remote sensing of water vapor and high clouds. Based on our analysis of two years# measurements with these channels, we have found that reliable observations of water vapor and high clouds on regional and global scales can be made. We will present results on daily, seasonal and annual variations of water vapor and high clouds.
Surface based near-infrared cloud spectral transmission measurements from a recent precipitation/cloud physics field study are used to determine cloud physical properties and relate them to other remote sensing and in situ measurements. Asymptotic formulae provide an effective means of closely approximating the qualitative and quantitative behavior of transmission computed by more laborious detailed methods. Relationships derived from asymptotic formulae are applied to measured transmission spectra to test objectively the internal consistency of data sets acquired during the field program and they confirmed the quality of the measurements. These relationships appear to be very useful in themselves, not merely as a quality control measure, but also a potentially valuable remote-sensing technique in its own right. Additional benefits from this analysis have been the separation of condensed water (cloud) transmission and water vapor transmission and the development of a method to derive cloud liquid water content.
As operational numerical weather prediction is performed at increasingly finer spatial resolution, precipitation traditionally represented by sub-grid scale parameterization schemes is now being calculated explicitly through the use of single- or multi-moment, bulk water microphysics schemes. As computational resources grow, the real-time application of these schemes is becoming available to a broader audience, ranging from national meteorological centers to their component forecast offices. A need for improved quantitative precipitation forecasts has been highlighted by the United States Weather Research Program, which advised that gains in forecasting skill will draw upon improved simulations of clouds and cloud microphysical processes. Investments in space-borne remote sensing have produced the NASA A-Train of polar orbiting satellites, specially equipped to observe and catalog cloud properties. The NASA CloudSat instrument, a recent addition to the A-Train and the first 94 GHz radar system operated in space, provides a unique opportunity to compare observed cloud profiles to their modeled counterparts. Comparisons are available through the use of a radiative transfer model (QuickBeam), which simulates 94 GHz radar returns based on the microphysics of cloudy model profiles and the prescribed characteristics of their constituent hydrometeor classes. CloudSat observations of snowfall are presented for a case in the central United States, with comparisons made to precipitating clouds as simulated by the Weather Research and Forecasting Model and the Goddard single-moment microphysics scheme. An additional forecast cycle is performed with a temperature-based parameterization of the snow distribution slope parameter, with comparisons to CloudSat observations provided through the QuickBeam simulator.
Many factors control the ice supersaturation and microphysical properties in cirrus clouds. We explore the effects of dynamic forcing, ice nucleation mechanisms, and ice crystal growth rate on the evolution and distribution of water vapor and cloud properties in nighttime cirrus clouds using a one-dimensional cloud model with bin microphysics and remote sensing measurements obtained at the Department of Energy's Atmospheric Radiation Measurement (ARM) Climate Research Facility located near Lamont, OK. We forced the model using both large-scale vertical ascent and, for the first time, mean mesoscale velocity derived from radar Doppler velocity measurements. Both heterogeneous and homogeneous nucleation processes are explored, where a classical theory heterogeneous scheme is compared with empirical representations. We evaluated model simulations by examining both bulk cloud properties and distributions of measured radar reflectivity, lidar extinction, and water vapor profiles, as well as retrieved cloud microphysical properties. Our results suggest that mesoscale variability is the primary mechanism needed to reproduce observed quantities. Model sensitivity to the ice growth rate is also investigated. The most realistic simulations as compared with observations are forced using mesoscale waves, include fast ice crystal growth, and initiate ice by either homogeneous or heterogeneous nucleation. Simulated ice crystal number concentrations (tens to hundreds particles per liter) are typically two orders of magnitude smaller than previously published results based on aircraft measurements in cirrus clouds, although higher concentrations are possible in isolated pockets within the nucleation zone.
The Smoke, Clouds, and Radiation-Brazil (SCAR-B) field project took place in the Brazilian Amazon and cerrado regions in August-September 1995 as a collaboration between Brazilian and American scientists. SCAR-B, a comprehensive experiment to study biomass burning, emphasized measurements of surface biomass, fires, smoke aerosol and trace gases, clouds, and radiation. their climatic effects, and remote sensing from aircraft and satellites. It included aircraft and ground-based in situ measurements of smoke emission factors and the compositions, sizes, and optical properties of the smoke particles; studies of the formation of ozone; the transport and evolution of smoke; and smoke interactions with water vapor and clouds. This overview paper introduces SCAR-B and summarizes some of the main results obtained so far. (1) Fires: measurements of the size distribution of fires, using the 50 m resolution MODIS Airborne Simulator, show that most of the fires are small (e.g. 0.005 square km), but the satellite sensors (e.g., AVHRR and MODIS with I km resolution) can detect fires in Brazil which are responsible for 60-85% of the burned biomass: (2) Aerosol: smoke particles emitted from fires increase their radius by as much as 60%, during their first three days in the atmosphere due to condensation and coagulation, reaching a mass median radius of 0.13-0.17 microns: (3) Radiative forcing: estimates of the globally averaged direct radiative forcing due to smoke worldwide, based on the properties of smoke measured in SCAR-B (-O.l to -0.3 W m(exp -2)), are smaller than previously modeled due to a lower single-scattering albedo (0.8 to 0.9), smaller scattering efficiency (3 square meters g(exp -2) at 550 nm), and low humidification factor; and (4) Effect on clouds: a good relationship was found between cloud condensation nuclei and smoke volume concentrations, thus an increase in the smoke emission is expected to affect cloud properties. In SCAR-B, new techniques were developed for deriving the absorption and refractive index of smoke from ground-based remote sensing. Future spaceborne radiometers (e.g., MODIS on the Earth Observing System), simulated on aircraft, proved to be very useful for monitoring smoke properties, surface properties, and the impacts of smoke on radiation and climate.
The paper presents a review of the far infrared (FIR) properties of the Earth's atmosphere, and the role of these properties in climate. These properties have been relatively poorly understood, and it is one of the purposes of this review to demonstrate that, in recent years, we have made great strides in improving this understanding. Seen from space, the Earth is a cool object, with an effective emitting temperature of about 255 K. This contrasts with a global mean surface temperature of ~288 K, and is due primarily to strong absorption of outgoing longwave energy by water vapour, carbon dioxide and clouds (especially ice). A large fraction of this absorption occurs in the FIR, and so the Earth is effectively a FIR planet. The FIR is important in a number of key climate processes, for example the water vapour and cloud feedbacks (especially ice clouds). The FIR is also a spectral region which can be used to remotely sense and retrieve atmospheric composition in the presence of ice clouds. Recent developments in instrumentation have allowed progress in each of these areas, which are described, and proposals for a spaceborne FIR instrument are being formulated. It is timely to review the FIR properties of the clear and cloudy atmosphere, the role of FIR processes in climate, and its use in observing our planet from space.
Sites described as tropical montane cloud forests differ greatly, in part because observers tend to differ in their opinion as to what constitutes frequent and prolonged immersion in cloud. This definitional difficulty interferes with hydrologic analyses, assessments of environmental impacts on ecosystems, and biogeographical analyses of cloud forest communities and species. Quantitative measurements of cloud immersion can be obtained on site, but the observations are necessarily spatially limited, although well-placed observers can examine 10 50 km of a mountain range under rainless conditions. Regional analyses, however, require observations at a broader scale. This chapter discusses remote sensing and modeling approaches that can provide quantitative measures of the spatiotemporal patterns of cloud cover and cloud immersion in tropical mountain ranges. These approaches integrate remote sensing tools of various spatial resolutions and frequencies of observation, digital elevation models, regional atmospheric models, and ground-based observations to provide measures of cloud cover, cloud base height, and the intersection of cloud and terrain. This combined approach was applied to the Monteverde region of northern Costa Rica to illustrate how the proportion of time the forest is immersed in cloud may vary spatially and temporally. The observed spatial variation was largely due to patterns of airflow over the mountains. The temporal variation reflected the diurnal rise and fall of the orographic cloud base, which was influenced in turn by synoptic weather conditions, the seasonal movement of the Intertropical Convergence Zone and the north-easterly trade winds. Knowledge of the proportion of the time that sites are immersed in clouds should facilitate ecological comparisons and biogeographical analyses, as well as land use planning and hydrologic assessments in areas where intensive on-site work is not feasible.
The Multi-angle Imaging SpectroRadiometer (MISR) instrument has been collecting global Earth data from NASA's Terra satellite since February 2000. With its nine along-track view angles, four visible/near-infrared spectral bands, intrinsic spatial resolution of 275 m, and stable radiometric and geometric calibration, no instrument that combines MISR's attributes has previously flown in space. The more than 10-year (and counting) MISR data record provides unprecedented opportunities for characterizing long-term trends in aerosol, cloud, and surface properties, and includes 3-D textural information conventionally thought to be accessible only to active sensors.
Satellite observations of low-level clouds have challenged the assumption that adiabatic liquid water content combined with constant physical thickness will lead to a negative cloud optics feedback in a decadal climate change. We explore the reasons for the satellite results using four years of surface remote sensing data from the Atmospheric Radiation Measurement Program Cloud and Radiation Testbed site in the Southern Great Plains of the United States. We find that low cloud liquid water path is approximately invariant with temperature in winter but decreases strongly with temperature in summer, consistent with the satellite inferences at this latitude. This behavior occurs because liquid water content shows no detectable temperature dependence while cloud physical thickness decreases with warming. Thinning of clouds with warming is observed on seasonal, synoptic, and diurnal time scales; it is most obvious in the warm sectors of baroclinic waves. Although cloud top is observed to slightly descend with warming, the primary cause of thinning is the ascent of cloud base due to the reduction in surface relative humidity and the concomitant increase in the lifting condensation level of surface air. Low cloud liquid water path is not observed to be a continuous function of temperature. Rather, the behavior we observe is best explained as a transition in the frequency of occurrence of different boundary layer types: At cold temperatures, a mixture of stratified and convective boundary layers is observed, leading to a broad distribution of liquid water path values, while at warm temperatures, only convective boundary layers with small liquid water paths, some of them decoupled, are observed. Our results, combined with the earlier satellite inferences, imply that the commonly quoted 1.50 C lower limit for the equilibrium global climate sensitivity to a doubling of CO2, which is based on models with near-adiabatic liquid water behavior and constant physical thickness, should be revised upward.
Satellite observations of low-level clouds have challenged the assumption that adiabatic liquid water content combined with constant physical thickness will lead to a negative cloud optics feedback in a decadal climate change. We explore the reasons for the satellite results using four years of surface remote sensing data from the Atmospheric Radiation Measurement Program Cloud and Radiation Testbed site in the Southern Great Plains of the United States. We find that low cloud liquid water path is approximately invariant with temperature in winter but decreases strongly with temperature in summer, consistent with the satellite inferences at this latitude. This behavior occurs because liquid water content shows no detectable temperature dependence while cloud physical thickness decreases with warming. Thinning of clouds with warming is observed on seasonal, synoptic, and diurnal time scales; it is most obvious in the warm sectors of baroclinic waves. Although cloud top is observed to slightly descend with warming, the primary cause of thinning, is the ascent of cloud base due to the reduction in surface relative humidity and the concomitant increase in the lifting condensation level of surface air. Low cloud liquid water path is not observed to be a continuous function of temperature. Rather, the behavior we observe is best explained as a transition in the frequency of occurrence of different boundary layer types. At cold temperatures, a mixture of stratified and convective boundary layers is observed, leading to a broad distribution of liquid water path values, while at warm temperatures, only convective boundary layers with small liquid water paths, some of them decoupled, are observed. Our results, combined with the earlier satellite inferences, imply that the commonly quoted 1.5C lower limit for the equilibrium global climate sensitivity to a doubling of CO2 which is based on models with near-adiabatic liquid water behavior and constant physical thickness, should be revised upward.
Associations between cloud properties and aerosol loading are frequently observed in products derived from satellite measurements. These observed trends between clouds and aerosol optical depth suggest aerosol modification of cloud dynamics, yet there are uncertainties involved in satellite retrievals that have the potential to lead to incorrect conclusions. Two of the most challenging problems are addressed here: the potential for retrieved aerosol optical depth to be cloud-contaminated, and as a result, artificially correlated with cloud parameters; and the potential for correlations between aerosol and cloud parameters to be erroneously considered to be causal. Here these issues are tackled directly by studying the effects of the aerosol on convective clouds in the tropical Atlantic Ocean using satellite remote sensing, a chemical transport model, and a reanalysis of meteorological fields. Results show that there is a robust positive correlation between cloud fraction or cloud top height and the aerosol optical depth, regardless of whether a stringent filtering of aerosol measurements in the vicinity of clouds is applied, or not. These same positive correlations emerge when replacing the observed aerosol field with that derived from a chemical transport model. Model-reanalysis data is used to address the causality question by providing meteorological context for the satellite observations. A correlation exercise between the full suite of meteorological fields derived from model reanalysis and satellite-derived cloud fields shows that observed cloud top height and cloud fraction correlate best with model pressure updraft velocity and relative humidity. Observed aerosol optical depth does correlate with meteorological parameters but usually different parameters from those that correlate with observed cloud fields. The result is a near-orthogonal influence of aerosol and meteorological fields on cloud top height and cloud fraction. The results strengthen the case that the aerosol does play a role in invigorating convective clouds.
There is an increased interest in detecting and estimating the amount of falling snow reaching the Earths surface in order to fully capture the global atmospheric water cycle. An initial step toward global spaceborne falling snow algorithms for current and future missions includes determining the thresholds of detection for various active and passive sensor channel configurations and falling snow events over land surfaces and lakes. In this paper, cloud resolving model simulations of lake effect and synoptic snow events were used to determine the minimum amount of snow (threshold) that could be detected by the following instruments: the W-band radar of CloudSat, Global Precipitation Measurement (GPM) Dual-Frequency Precipitation Radar (DPR)Ku- and Ka-bands, and the GPM Microwave Imager. Eleven different nonspherical snowflake shapes were used in the analysis. Notable results include the following: 1) The W-band radar has detection thresholds more than an order of magnitude lower than the future GPM radars; 2) the cloud structure macrophysics influences the thresholds of detection for passive channels (e.g., snow events with larger ice water paths and thicker clouds are easier to detect); 3) the snowflake microphysics (mainly shape and density)plays a large role in the detection threshold for active and passive instruments; 4) with reasonable assumptions, the passive 166-GHz channel has detection threshold values comparable to those of the GPM DPR Ku- and Ka-band radars with approximately 0.05 g *m(exp -3) detected at the surface, or an approximately 0.5-1.0-mm * h(exp -1) melted snow rate. This paper provides information on the light snowfall events missed by the sensors and not captured in global estimates.
A simultaneous examination was conducted of cirrus clouds in the FIRE Cirrus IFO-I on 10/28/86 using a multitude of remote sensing and in-situ measurements. The focus is cirrus cloud radiative properties and their relationship to cloud microphysics. A key element is the comparison of radiative transfer model calculations and varying measured cirrus radiative properties (emissivity, reflectance vs. wavelength, reflectance vs. viewing angle). As the number of simultaneously measured cloud radiative properties and physical properties increases, more sharply focused tests of theoretical models are possible.
Geostationary satellite imagers, such as those of the Geostationary Operational Environmental Satellite (GOES) series, have been observing severe convection at 15–60-minute intervals for over 40 years. When properly assessed, such a data record can be valuable in efforts of estimating severe storm risk throughout the diurnal cycle based on automated detection of patterns consistently found atop severe storms. Furthermore, environmental conditions favorable for severe weather are well-known and are thought to be represented well by modern reanalysis products. Promoting resilience against such hazards on local and global scales is a chief goal the NASA Disasters program, which seeks to encourage use of satellite observations to mitigate risk. For instance, hail is the costliest severe weather hazard across the globe in terms of insured loss, but reporting inconsistencies for hail events globally make it difficult to develop models that can quantify the risk. Satellite observation and model reanalysis taken together have the potential to, with reasonable skill and specificity, characterize environmental conditions that are favorable for hazardous weather, and thereby enable creation of hazard climatologie. Such climatologies are particularly useful over regions without extensive radar networks or storm reporting. By mapping the multivariate combination of observed cloud features and reanalysis environmental parameters/indices to United States Next Generation Weather Radar (NEXRAD) radar-estimated Maximum Expected Size of Hail (MESH) by way of a deep neural network (DNN), estimates of likelihood for potentially severe hail can be produced. Such estimates are of greater complexity and efficiency than could be performed with previous multivariate or logistic regression analyses for observed points within convective systems. Statistical distributions of convective parameters from satellite and reanalysis are shown to highlight non-severe/severe class separation for well-known hailstorm predictors, e.g., overshooting cloud top characteristics, deep-layer wind shear, mid-level stability, helicity, and convective inhibition. These complex, multivariate predictor relationships are exploited within a DNN, which can efficiently produce a quantitative hail risk metric with better than 70% detection rate and under 30% false alarms. These hail classifications can then be aggregated across the satellite record to yield a hazard climatology for hail frequency and severity – knowledge of which is of particular interest to those who manage risk (e.g., insurers) and are seeking opportunities to identify hail-prone regions, particularly in developing nations. This NASA study uses satellite observations and model parameters in a DNN to perform climatological hailstorm analysis in support of catastrophe model development, with the hope of promoting risk resilience particularly in regions without adequate weather radar coverage.
This research program in support of Project FIRE (First ISCCP Regional Experiment) involved two efforts. The results of the first effort, which were in direct support of the Extended Time Observations (ETO) component, are described here. Over the period from June 1990 through May 1991, our remote sensing systems were applied to providing ground-truth cirrus cloud observations for a total of 71 NOAA polar orbiting satellite overpasses. (Chronological tables of this effort are provided.) The primary remote sensor was a dual-polarization ruby (0.694 microns wavelength) lidar, although mid-way through the program we added a number of radiometers to assess the surface radiation budget and cirrus cloud infrared emittance, and some supplemental observations from a Ka-band (8.6 mm) radar were also collected. These studies were conducted from the Facility for Atmospheric Remote Sensing (FARS) at 40 degrees 46 minutes 00 seconds north latitude and 111 degrees 49 minutes 38 seconds east longitude. We also investigated the unusual characteristics of a subset of ETO case studies involving cirrus that generated solar and lunar corona displays. As we reported recently (reprint attached), these cirrus were atypically high and cold in relation to our total midlatitude cloud sample, and were comprised of unexpectedly small ice crystals from 10 to 30 microns in dimension. This finding lends some credence to the so-called cirrus small particle radiative anomaly, but only for very cold (less than -60 C) cirrus clouds. In a supplement, we will describe the design and testing of a prototype cirrus cloud polar nephelometer, which we constructed as part of our second research effort, to allow scattering phase functions to be obtained in future in situ cirrus research.