Satellite Cloud Research Articles

Recent trends of the tropical hydrological cycle inferred from Global Precipitation Climatology Project and International Satellite Cloud Climatology Project data

Received 15 October 2010; revised 4 January 2011; accepted 7 February 2011; published 7 May 2011. [1] Scores of modeling studies have shown that increasing greenhouse gases in the atmosphere impact the global hydrologic cycle; however, disagreements on regional scales are large, and thus the simulated trends of such impacts, even for regions as large as the tropics, remain uncertain. The present investigation attempts to examine such trends in the observations using satellite data products comprising Global Precipitation Climatology Project precipitation and International Satellite Cloud Climatology Project cloud and radiation. Specifically, evolving trends of the tropical hydrological cycle over the last 20–30 years were identified and analyzed. The results show (1) intensification of tropical precipitation in the rising regions of the Walker and Hadley circulations and weakening over the sinking regions of the associated overturning circulation; (2) poleward shift of the subtropical dry zones (up to 2° decade −1 in June‐July‐August (JJA) in the Northern Hemisphere and 0.3–0.7° decade −1 in June‐July‐August and September‐ October‐November in the Southern Hemisphere) consistent with an overall broadening of the Hadley circulation; and (3) significant poleward migration (0.9–1.7° decade −1 ) of cloud boundaries of Hadley cell and plausible narrowing of the high cloudiness in the Intertropical Convergence Zone region in some seasons. These results support findings of some of the previous studies that showed strengthening of the tropical hydrological cycle and expansion of the Hadley cell that are potentially related to the recent global warming trends.

Changes in extratropical storm track cloudiness 1983–2008: observational support for a poleward shift

Climate model simulations suggest that the extratropical storm tracks will shift poleward as a consequence of global warming. In this study the northern and southern hemisphere storm tracks over the Pacific and Atlantic ocean basins are studied using observational data, primarily from the International Satellite Cloud Climatology Project, ISCCP. Potential shifts in the storm tracks are examined using the observed cloud structures as proxies for cyclone activity. Different data analysis methods are employed, with the objective to address difficulties and uncertainties in using ISCCP data for regional trend analysis. In particular, three data filtering techniques are explored; excluding specific problematic regions from the analysis, regressing out a spurious viewing geometry effect, and excluding specific cloud types from the analysis. These adjustments all, to varying degree, moderate the cloud trends in the original data but leave the qualitative aspects of those trends largely unaffected. Therefore, our analysis suggests that ISCCP data can be used to interpret regional trends in cloudiness, provided that data and instrumental artefacts are recognized and accounted for. The variation in magnitude between trends emerging from application of different data correction methods, allows us to estimate possible ranges for the observational changes. It is found that the storm tracks, here represented by the extent of the midlatitude-centered band of maximum cloud cover over the studied ocean basins, experience a poleward shift as well as a narrowing over the 25 year period covered by ISCCP. The observed magnitudes of these effects are larger than in current generation climate models (CMIP3). The magnitude of the shift is particularly large in the northern hemisphere Atlantic. This is also the one of the four regions in which imperfect data primarily prevents us from drawing firm conclusions. The shifted path and reduced extent of the storm track cloudiness is accompanied by a regional reduction in total cloud cover. This decrease in cloudiness can primarily be ascribed to low level clouds, whereas the upper level cloud fraction actually increases, according to ISCCP. Independent satellite observations of radiative fluxes at the top of the atmosphere are consistent with the changes in total cloud cover. The shift in cloudiness is also supported by a shift in central position of the mid-troposphere meridional temperature gradient. We do not find support for aerosols playing a significant role in the satellite observed changes in cloudiness. The observed changes in storm track cloudiness can be related to local cloud-induced changes in radiative forcing, using ERBE and CERES radiative fluxes. The shortwave and the longwave components are found to act together, leading to a positive (warming) net radiative effect in response to the cloud changes in the storm track regions, indicative of positive cloud feedback. Among the CMIP3 models that simulate poleward shifts in all four storm track areas, all but one show decreasing cloud amount on a global mean scale in response to increased CO2 forcing, further consistent with positive cloud feedback. Models with low equilibrium climate sensitivity to a lesser extent than higher-sensitivity models simulate a poleward shift of the storm tracks.

Reconciling the global terrestrial water budget using satellite remote sensing

Recent retrievals of multiple satellite products for each component of the terrestrial water cycle provide an opportunity to estimate the water budget globally. In this study, we estimate the water budget from satellite remote sensing over ten global river basins for 2003–2006. We use several satellite and non-satellite precipitation ( P) and evapo-transpiration ( ET) products in this study. The satellite precipitation products are the GPCP, TRMM, CMORPH and PERSIANN. For ET, we use four products generated from three retrieval models (Penman–Monteith (PM), Priestley–Taylor (PT) and the Surface Energy Balance System (SEBS)) with data inputs from the Earth Observing System (EOS) or the International Satellite Cloud Climatology Project (ISCCP) products. GPCP precipitation and PM (ISCCP) ET have less bias and errors over most of the river basins. To estimate the total water budget from satellite data for each basin, we generate merged products for P and ET by combining the four P and four ET products using weighted values based on their errors with respect to non-satellite merged product. The water storage change component is taken from GRACE satellite data, which are used directly with a single pre-specified error value. In the absence of satellite retrievals of river discharge, we use in-situ gauge measurements. Closure of the water budget over the river basins from the combined satellite and in-situ discharge products is not achievable with errors of the order of 5–25% of mean annual precipitation. A constrained ensemble Kalman filter is used to close the water budget and provide a constrained best-estimate of the water budget. The non-closure error from each water budget component is estimated and it is found that the merged satellite precipitation product carries most of the non-closure error.

Analysis of the biases in the downward shortwave surface flux in the GFDL CM2.1 general circulation model

[1] Simulations of downward shortwave surface fluxes by the coupled Geophysical Fluid Dynamics Laboratory (GFDL) CM2.1 general circulation model are compared against climatology derived from the Baseline Surface Radiation Network (BSRN), Global Energy Balance Archive, and International Satellite Cloud Climatology Project ISCCP-FD data sets. The spatial pattern of the model's biases is evaluated. An investigation is made of how these relate to accompanying biases in total cloud amount and aerosol optical depth and how they affect the surface temperature simulation. Comparing CM2.1's clear-sky fluxes against BSRN site values, for European, Asian, and North American locations, there are underestimates in the direct and overestimates in the diffuse, resulting in underestimates in the total flux. These are related to overestimates of sulfate aerosol optical depth, arising owing to the behavior of the parameterization function for hygroscopic growth of these aerosols at very high relative humidity. Contrastingly, flux overestimate biases at lower latitude locations are associated with underestimates in sea-salt and carbonaceous aerosol amounts. All-sky flux biases consist of underestimates for North America, Eurasia, southern Africa, and northern oceanic regions and overestimates for the Amazon region, equatorial Africa, off the west coast of the Americas, and southern oceanic regions. These biases show strong correlations with cloud amount biases. There are modest correlations of the flux biases with cool surface temperature biases over North America and Eurasia, warm biases over the Amazon region, and cool (warm) biases over the northern (southern) oceanic regions. Analyses assuming nonhygroscopicity illustrate that there is a reduction of surface temperature biases accompanying a reduction of sulfate aerosol optical depth biases, whereas a more significant improvement in the temperature simulation requires refining the model's simulation of cloudiness.

Cloud variations under subtropical high conditions

The cloud variations under subtropical high (STH) conditions during summers over a ten-year period are studied using combined data from the International Satellite Cloud Climatology Project and the National Centers for Environmental Prediction. The results reveal that clouds mainly experience an isolated evolution in the STHs, which is designated in this study by the 1540 gpm geopotential lines at 850 hPa. In the STH domain throughout the Northern Hemisphere, the average amount of total clouds exceeds 30%. Low clouds dominate in the STH domain, contributing over 60% of total cloud amount within the Pacific subtropical high and over 40% within the Atlantic subtropical high. The prevalence of low clouds in above regions is determined by the circulation pattern around 150°–180°E and 850 hPa, which suppresses both the upward development of the cloud tops and the water vapor divergences near the surface. Furthermore, clouds present great geographical incoherence within the STH domain. In the eastern STHs, the amount of middle and low clouds increases to peak in the early morning and decreases to a trough in the afternoon, while the amount of high clouds remains stable throughout the day. Conversely, in the western STHs, the diurnal amplitude of low and middle clouds is less than three, while high clouds dramatically reach the maximum in the afternoon and drop to the minimum in the evening. Among the nine cloud categories, stratocumulus clouds with greater optical thickness account for the most under STH conditions, no matter their occurrence or amount, causing more shortwave cloud radiative forcing to cool the local atmosphere and surface as a consequence.

Seasonal-latitudinal variation of the eastward-propagating diurnal tide with zonal wavenumber 3 in the MLT: Influences of heating and background wind distribution

This study demonstrates how the mean zonal wind affects the propagation of tidal energy from the troposphere to the mesosphere-lower thermosphere (MLT) region (60–120km). We diagnose the seasonal-latitudinal variation of the eastward-propagating diurnal tide with zonal wavenumber 3 (DE3), which is a prominent tidal component and agrees well between model and observation in recent studies. It is shown that during the course of a year the seasonal-latitudinal variation of mean zonal wind modulates and filters DE3 energy propagating upwards from tropospheric radiative and latent heating sources. Our results are based upon ISCCP and TRMM troposphere heating rates, SABER-derived zonal mean gradient wind, SABER tidal signatures and GSWM (the global-scale wave model) diagnoses. The tropospheric tidal heating rates are derived from ISCCP (International Satellite Cloud Climatology Project) radiative fluxes and TRMM (Tropical Rainfall Measuring Mission) latent heating profiles together with TRMM rainfall rates. The zonal mean winds are derived from measurements of geopotential height from the SABER (sounding of the atmosphere with broadband emission radiometry) instrument on the TIMED (thermosphere, ionosphere, mesosphere energetics and dynamics) spacecraft, and the tidal signatures are determined from SABER temperature measurements.

Critical Evaluation of the ISCCP Simulator Using Ground-Based Remote Sensing Data

Abstract Given the known shortcomings in representing clouds in global climate models (GCMs), comparisons with observations are critical. The International Satellite Cloud Climatology Project (ISCCP) diagnostic products provide global descriptions of cloud-top pressure and column optical depth that extend over multiple decades. Given the characteristics of the ISCCP product, the model output must be converted into what the ISCCP algorithm would diagnose from an atmospheric column with similar physical characteristics. This study evaluates one component of this so-called ISCCP simulator by comparing ISCCP results with simulated ISCCP diagnostics that are derived from data collected at the Atmospheric Radiation Measurement Program (ARM) Southern Great Plains (SGP) Climate Research Facility. It is shown that if a model were to simulate the cloud radiative profile with the same accuracy as can be derived from the ARM data, the likelihood of that occurrence being classified with similar cloud-top pressure and optical depth as ISCCP would range from 30% to 70% depending on optical depth. The ISCCP simulator improved the agreement of cloud-top pressure between ground-based remote sensors and satellite observations, and we find only minor discrepancies because of the parameterization of cloud-top pressure in the ISCCP simulator. The differences seem to be primarily due to discrepancies between satellite and ground-based sensors in the visible optical depth. The source of the optical depth bias appears to be due to subpixel cloud field variability in the retrieval of optical depths from satellite sensors. These comparisons suggest that caution should be applied to comparisons between models and ISCCP observations until the differences in visible optical depths are fully understood. The simultaneous use of ground-based and satellite retrievals in the evaluation of model clouds is encouraged.

Influence of cloud properties on satellite total ozone observations

[1] Clouds represent one of the most important atmospheric factors that can significantly reduce the accuracy of satellite total ozone column (TOC) data. The influence of clouds on the TOC retrieval from satellites is usually assessed by means of theoretical studies using radiative transfer models. In contrast, few experimental results were found in the literature about the effects of the cloud properties on the differences between satellite TOC data and accurate ground-based measurements. This paper is focused on the study of the joined influence of the cloud properties under different satellite viewing conditions on the TOC data derived from four satellite instruments (GOME/ERS-2, GOME-2/MetOp-A, SCIAMACHY/Envisat, and OMI/Aura) using as reference five Brewer spectrophotometers in the Iberian Peninsula. Satellite TOC data under cloud-free and cloudy conditions are compared with ground-based measurements for the time period 2006 to 2009. The TOC products derived using the Differential Optical Absorption Spectroscopy (DOAS) technique, GOME, GOME-2, SCIAMACHY and OMI-DOAS, show a notable underestimation for cloudy conditions that clearly decreases with increasing Sun zenith angles (SZA). For those DOAS TOC products, the viewing zenith angle (VZA) also shows a significant influence on the satellite-Brewer differences, mainly for the west pixels and cloud-free conditions. The OMI-TOMS TOC product under cloud-free and cloudy conditions presents a more stable pattern for both SZA and VZA values, but this result must be interpreted carefully because OMI-TOMS classifies an unrealistic high number of scenes as cloud-free (∼57% of all). The analysis of the relative TOC differences as a function of the satellite cloud fraction indicates that SCIAMACHY and OMI-DOAS products have a large difference between the curves corresponding to low and high SZA cases. The two GOME algorithms and, mainly, the OMI-TOMS algorithm present a remarkable stability for all cloud conditions.

Relating tropical ocean clouds to moist processes using water vapor isotope measurements

Abstract. We examine the co-variations of tropospheric water vapor, its isotopic composition and cloud types and relate these distributions to tropospheric mixing and distillation models using satellite observations from the Aura Tropospheric Emission Spectrometer (TES) over the summertime tropical ocean. Interpretation of these process distributions must take into account the sensitivity of the TES isotope and water vapor measurements to variations in cloud, water, and temperature amount. Consequently, comparisons are made between cloud-types based on the International Satellite Cloud Climatology Project (ISSCP) classification; these are clear sky, non-precipitating (e.g., cumulus), boundary layer (e.g., stratocumulus), and precipitating clouds (e.g. regions of deep convection). In general, we find that the free tropospheric vapor over tropical oceans does not strictly follow a Rayleigh model in which air parcels become dry and isotopically depleted through condensation. Instead, mixing processes related to convection as well as subsidence, and re-evaporation of rainfall associated with organized deep convection all play significant roles in controlling the water vapor distribution. The relative role of these moisture processes are examined for different tropical oceanic regions.

Spiral band model for locating Tropical Cyclone centers

A novel spiral band model is presented instead of existing curvilinear ones to locate Tropical Cyclone (TC) centers automatically. Particle swarm optimization is used to optimize the model parameters. Experimental results on infrared satellite cloud images show that the model can achieve the best average error of 0.1223° (about 13.58 km) on both latitude and longitude in comparison with the best track data. It can assist forecasters in locating TC centers by providing objective information.

Satellite Cloud Image Registration by Combining Curvature Shape Representation with Particle Swarm Optimization

A new algorithm which is used to match the satellite cloud image feature point is proposed. The new algorithm is proposed by combining corner detection with curvature scale space. The new algorithm can accurately extract the satellite cloud image corner points in different positions and directions. In order to accurately match the corner points of two source images, an overall restricted condition, which combines angle difference, gray level difference, relative distance and normalized correlation coefficient of the two matched corner points, is used to improve the matching accuracy. Finally, particle swarm optimization algorithm is used to obtain the optimal registration parameters. The optimal registration parameters are used to accurately match the two source images. The experimental results show that the proposed algorithm can accurately match the satellite cloud images and better than traditional image registration methods.

Analysis the Typhoon Eyes of Megi from MTSAT Satellite Cloud Images with 3-D Profile Reconstruction

In this paper, image reconstruction technique (IRT) is used to reconstruct a 3-D profile of typhoons from MTSAT satellite cloud image data and based on a 1691 MHz receiver and iDAP system. The satellite cloud image data gives a single line profile slicing from a surface cloud image which does match the typhoon distribution. The line profile is presented with the temperature of the cloud top. The 3-D profiles of typhoons are constructed with the surface cloud images and the temperatures. IRT is conducted using the data of the 2010 Megi event. The typhoon feature is studied and the various typhoon eyes in three time intervals are analyzed. An effective early-warning system may become feasible based on this work.Request access from your librarian to read this article's full text.

Performance of Observation-Based Prediction Algorithms for Very Short-Range, Probabilistic Clear-Sky Condition Forecasting

AbstractVery short-range sky condition forecasts are produced to support a variety of military, civil, and commercial activities. In this investigation, six advanced, observation (obs)-based prediction algorithms were developed and tested that generated probabilistic sky condition forecasts for 1-, 2-, 3-, 4-, and 5-h forecast intervals, for local and regional target types, in six geographic regions within the continental United States. Three of the methods were based on predictive learning algorithms including neural network, random forest, and regression tree. The other three methods were statistical techniques including a k–nearest neighbor algorithm, a classifier based on the Bayes decision rule, and a multialgorithm ensemble. The performances of these six algorithms were compared with forecasts from three benchmark methods: basic persistence, the climatological-expectancy-of-persistence, and satellite cloud climatology. The obs database for each forecast target was composed of a multiyear, half-hourly time series of atmospheric parameters that included cloud features extracted from weather satellite imagery and meteorological variables extracted or derived from data assimilation–based model analyses generated by NCEP’s Eta Data Assimilation System. The performances of the advanced prediction algorithms exceeded those of the benchmarks at all five forecast intervals for both target types in all regions, on the basis of a group of metrics that included receiver operating characteristic score, sharpness, accuracy, expected best cost, and reliability.

Estimation of Liquid Cloud Properties that Conserve Total-Scene Reflectance Using Satellite Measurements

Abstract A new method of deriving statistical moments related to the distribution of liquid water path over partially cloudy scenes is tested using a satellite cloud climatology. The method improves the ability to reconstruct total-scene visible reflectance when compared with an approach that relies on valid liquid water path retrievals, and thus it maintains physical consistency with the primary satellite observations when deriving cloud climatologies. A global application of the new method finds a mean bias of −0.008 ± 0.017 when reconstructing total-scene reflectance from liquid water path distributions, as compared with a bias of 0.05 ± 0.047 when using a conventional approach. Application of the method to a multidecadal cloud climatology suggests that this may provide a means of identifying data artifacts that could affect long-term cloud property trends. The conservation of reflectance plus the ease of applicability to various satellite datasets makes this method a valuable tool for model validation and comparison of satellite climatologies. Gaussian and gamma functions are used to approximate the distribution of horizontal subgrid-scale liquid water path for 1° × 1° scenes, and while both functions perform well for the majority of atmospheric conditions, it is found that the Gaussian distribution generates a negative bias for cases in which visible reflectance is very high and that neither function is able to represent liquid water path well in the few cases in which the observed distribution is bi- or multimodal.

Evaluation of a Statistical Model of Cloud Vertical Structure Using Combined CloudSat and CALIPSO Cloud Layer Profiles

Abstract A model of the three-dimensional distribution of clouds was developed from the statistics of cloud layer occurrence from the International Satellite Cloud Climatology Project (ISCCP) and the statistics of cloud vertical structure (CVS) from an analysis of radiosonde humidity profiles. The CVS model associates each cloud type, defined by cloud-top pressure of the topmost cloud layer and total column optical thickness, with a particular CVS. The advent of satellite cloud radar (CloudSat) and lidar [Cloud–Aerosol Lidar and Infrared Pathfinder Satellite Observations (CALIPSO)] measurements (together C&C) of CVS allows for a quantitative evaluation of this statistical model. The zonal monthly-mean cloud layer distribution from the ISCCP CVS agrees with that from C&C to within 10% (when normalized to the same total cloud amount). The largest differences are an overestimate of middle-level cloudiness in winter polar regions, an overestimate of cloud-top pressures of the highest-level clouds, especially in the tropics, and an underestimate of low-level cloud amounts over southern midlatitude oceans. A more severe test of the hypothesized relationship is made by comparing CVS for individual satellite pixels. The agreement of CVS is good for isolated low-level clouds and reasonably good when the uppermost cloud layer is a high-level cloud; however, the agreement is not good when the uppermost cloud layer is a middle-level cloud, even when ISCCP correctly locates cloud top. An improved CVS model combining C&C and ISCCP may require classification at spatial scales larger than individual satellite pixels.

An assessment of oceanic variability in the NCEP climate forecast system reanalysis

At the National Centers for Environmental Prediction (NCEP), a reanalysis of the atmosphere, ocean, sea ice and land over the period 1979–2009, referred to as the climate forecast system reanalysis (CFSR), was recently completed. The oceanic component of CFSR includes many advances: (a) the MOM4 ocean model with an interactive sea-ice, (b) the 6 h coupled model forecast as the first guess, (c) inclusion of the mean climatological river runoff, and (d) high spatial (0.5° × 0.5°) and temporal (hourly) model outputs. Since the CFSR will be used by many in initializing/validating ocean models and climate research, the primary motivation of the paper is to inform the user community about the saline features in the CFSR ocean component, and how the ocean reanalysis compares with in situ observations and previous reanalysis. The net ocean surface heat flux of the CFSR has smaller biases compared to the sum of the latent and sensible heat fluxes from the objectively analyzed air-sea fluxes (OAFlux) and the shortwave and longwave radiation fluxes from the International Satellite Cloud Climatology Project (ISCCP-FD) than the NCEP/NCAR reanalysis (R1) and NCEP/DOE reanalysis (R2) in both the tropics and extratropics. The ocean surface wind stress of the CFSR has smaller biases and higher correlation with the ERA40 produced by the European Centre for Medium-Range Weather Forecasts than the R1 and R2, particularly in the tropical Indian and Pacific Ocean. The CFSR also has smaller errors compared to the QuickSCAT climatology for September 1999 to October 2009 than the R1 and R2. However, the trade winds of the CFSR in the central equatorial Pacific are too strong prior to 1999, and become close to observations once the ATOVS radiance data are assimilated in late 1998. A sudden reduction of easterly wind bias is related to the sudden onset of a warm bias in the eastern equatorial Pacific temperature around 1998/1999. The sea surface height and top 300 m heat content (HC300) of the CFSR compare with observations better than the GODAS in the tropical Indian Ocean and extratropics, but much worse in the tropical Atlantic, probably due to discontinuity in the deep ocean temperature and salinity caused by the six data streams of the CFSR. In terms of climate variability, the CFSR provides a good simulation of tropical instability waves and oceanic Kelvin waves in the tropical Pacific, and the dominant modes of HC300 that are associated with El Nino and Southern Oscillation, Indian Ocean Dipole, Pacific Decadal Oscillation and Atlantic Meridional Overturning Circulation.

Comparison of Satellite Cloud Masks with Ceilometer Sky Conditions in Southern Finland

AbstractThe cloud mask is an essential product derived from satellite data. Whereas cloud analysis applications typically make use of information from cloudy pixels, many other applications require cloud-free conditions. For this reason many organizations have their own cloud masks tuned to serve their particular needs. Being a fundamental product, continuous quality monitoring and validation of these cloud masks are vital. This study evaluated the performance of the European Organisation for the Exploitation of Meteorological Satellites (EUMETSAT) Meteorological Products Extraction Facility cloud mask (MPEF), together with the Nowcasting Satellite Application Facility (SAFNWC) cloud masks provided by Météo-France (SAFNWC/MSG) and the Swedish Meteorological and Hydrological Institute (SAFNWC/PPS), in the high-latitude area of greater Helsinki in Finland. The first two used the Spinning Enhanced Visible and Infrared Imager (SEVIRI) instrument from the geostationary Meteosat-8 satellite, whereas the last used the Advanced Very High Resolution Radiometer (AVHRR) instrument on board the polar-orbiting NOAA satellite series. Ceilometer data from the Helsinki Testbed, an extensive observation network covering the greater Helsinki area in Finland, were used as reference data in the cloud mask comparison. A computational method, called bootstrapping, is introduced to account for the strong temporal and spatial correlation of the ceilometer observations. The method also allows the calculation of the confidence intervals (CI) for the results. This study comprised data from February and August 2006. In general, the SAFNWC/MSG algorithm performed better than MPEF. Differences were found especially in the early morning low cloud detection. The SAFNWC/PPS cloud mask performed very well in August, better than geostationary-based masks, but had problems in February when its performance was worse. The use of the CIs gave the results more depth, and their use should be encouraged.

Surface radiative fluxes over the pan‐Arctic land region: Variability and trends

Surface radiative fluxes are a key driver of the land surface hydrological cycle, but because in situ measurements are sparse, relatively little attention has been focused on their space‐time variations. Recent satellite data and atmospheric model reanalysis products have resulted in data sets that predict most or all terms in the surface energy budget and offer the opportunity to investigate variations in surface radiative fluxes. This study contributes the NASA Energy and Water Cycle Study (NEWS), which is intended to improve estimates of the terrestrial energy budget components for the hydrologic and ecological communities. We analyzed surface downward shortwave (DSW) and longwave radiation (DLW) and albedo over the pan‐Arctic domain from (1) the European Centre for Medium‐Range Weather Forecast (ECMWF) 40‐Year Reanalysis (ERA‐40), (2) the ECMWF Interim Reanalysis (ERA‐Interim), (3) the International Satellite Cloud Climatology Project‐Flux Data (ISCCP‐FD), and (4) a Temperature Index (TIND) scheme for the period from 1984 to 2006. As compared with in situ measurements, the reanalysis products provide better estimates of the DSW diurnal cycle than do the satellite product and temperature index scheme simulation. At the regional scale, all data sets have similar temporal patterns except for DLW in ISCCP and snow season albedo. In terms of dominant spatial variability, all data sets show large variability in the pan‐Arctic. In addition, DSW and DLW show a similar latitudinal gradient. However, the difference in albedo suggests a need of improvement for the reanalysis products. For a small number of stations with relatively long records, we analyzed long‐term trends in DSW and found a turning point between 1985 and 1990. Before that, a dimming period exists, whereas brightening occurred thereafter.

Earth like planets albedo variations versus continental landmass distribution

AbstractBy making use of real information about the continental and oceanic surface distribution of the Earth, and cloudiness data from the International Satellite Cloud Climatology Project (ISCCP), we have studied the large-scale cloudiness behavior according to latitude and surface types (ice, water, vegetation and desert). These empirical relationships are used here to reconstruct the possible cloud distribution of historical epochs of the Earth history such as Late Cretaceous (90 My ago) and Late Triasic (230 My ago) when the landmass distribution was different. This information can be used to simulate the photometric variability of these planets according to their different geographical distribution.

Assimilation of Satellite-Derived Skin Temperature Observations into Land Surface Models

Abstract Land surface (or “skin”) temperature (LST) lies at the heart of the surface energy balance and is a key variable in weather and climate models. In this research LST retrievals from the International Satellite Cloud Climatology Project (ISCCP) are assimilated into the Noah land surface model and Catchment land surface model (CLSM) using an ensemble-based, offline land data assimilation system. LST is described very differently in the two models. A priori scaling and dynamic bias estimation approaches are applied because satellite and model LSTs typically exhibit different mean values and variabilities. Performance is measured against 27 months of in situ measurements from the Coordinated Energy and Water Cycle Observations Project at 48 stations. LST estimates from Noah and CLSM without data assimilation (“open loop”) are comparable to each other and superior to ISCCP retrievals. For LST, the RMSE values are 4.9 K (CLSM), 5.5 K (Noah), and 7.6 K (ISCCP), and the anomaly correlation coefficients (R) are 0.61 (CLSM), 0.63 (Noah), and 0.52 (ISCCP). Assimilation of ISCCP retrievals provides modest yet statistically significant improvements (over an open loop, as indicated by nonoverlapping 95% confidence intervals) of up to 0.7 K in RMSE and 0.05 in the anomaly R. The skill of the latent and sensible heat flux estimates from the assimilation integrations is essentially identical to the corresponding open loop skill. Noah assimilation estimates of ground heat flux, however, can be significantly worse than open loop estimates. Provided the assimilation system is properly adapted to each land model, the benefits from the assimilation of LST retrievals are comparable for both models.