Cross-Comparison of MODIS and CloudSat Data as a Tool to Validate Local Cloud Cover Masks

This paper presents a cross-comparison of the data acquired by the MODIS and CloudSat sensors in order to understand the limit of the developed cloud-mask algorithm and to provide a quantitative validation assessment of cloud masks by using exclusively remotely sensed data. The analysis has been carried out by comparing both the intermediate levels of the cloud mask such as the brightness temperatures and the reflectance values for different channels, and the cloud mask itself with the cloud profiles as measured by the CloudSat sensor. The comparison between MODIS cloud tests and the CloudSat profiles indicates an agreement with hit rates (H) and Hanssen-Kuiper Skill Score (KSS) varying between 0.7 and 1.0 and 0.4 and 1.0, respectively. In this case, the low values of H and KSS are found due to the limitation of CloudSat to detect low clouds. The comparison between the cloud mask and the CloudSat profile determines H and KSS values between 0.6 and 1, except for one case. The CloudSat profile has also been compared with the Standard MODIS cloud mask in order to understand the improvement obtained in the use of local adapted thresholds. A comparison of MODIS and CALIPSO data is also presented

New look at the Earth's radiation balance from an A-train observational perspective, A

2010 Fall.Includes bibliographical references.The weather and climate of the Earth are driven by interactions of the longwave and shortwave radiation between the Earth's atmosphere and surface. Past studies have tried to derive the Earth radiative budget through the use of models and passive satellite sensors. These past efforts did not have information about the vertical distribution of cloud or aerosols within the atmosphere that significantly influence radiative transfer within the atmosphere. This problem was improved upon with the launch of CloudSat and CALIPSO in 2006. These satellites provide the information on the vertical distribution of clouds. From CloudSat, a fluxes and heating rates product was produced to study the radiative budget, but this was limited to some degree because of undetected clouds and aerosol that have non-negligible effects on the radiative balance. This study addresses these issues by combining CALIPSO and MODIS data with CloudSat to detect and obtain the properties of cloud and aerosol undetected by the CloudSat CPR. The combined data were used to create a cloud and aerosol mask that identified distributions of undetected cloud and aerosol globally and quantified their radiative effects both seasonally and annually. Low clouds were found to have the highest impacts of nearly -6 Wm-2. High clouds globally have little effect, trapping 1 Wm-2, with the majority of the impact in the tropics. Four case studies are presented to show how heating rates change in the vertical due low cloud, cirrus, precipitation, and aerosol. The cloud and aerosol mask was used to create seasonal global distributions of cloud radiative effect using all clouds detected by CloudSat and CALIPSO, and the direct effect of aerosols estimated at the TOA. Using fluxes at the top and bottom of the atmosphere global distributions of outgoing and incoming radiation are shown, and an annual radiation budget of the Earth is derived. Clouds globally are found to have a radiative forcing of -20 Wm-2 at the TOA. The radiative budget of the Earth is calculated in two ways; using normalized shortwave fluxes by the average solar daily insolation, and by changing the solar zenith angle to simulate the diurnal cycle. Finally, the product is validated by comparing the outgoing and surface fluxes with CERES and ISCPP flux products

The Cumulus and Stratocumulus CloudSat-CALIPSO Dataset (CASCCAD)

Low clouds continue to contribute greatly to the uncertainty in cloud feedback estimates. Depending on whether a region is dominated by cumulus (Cu) or stratocumulus (Sc) clouds, the interannual low-cloud feedback is somewhat different in both spaceborne and large-eddy simulation studies. Therefore, simulating the correct amount and variation of the Cu and Sc cloud distributions could be crucial to predict future cloud feedbacks. Here we document spatial distributions and profiles of Sc and Cu clouds derived from Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observations (CALIPSO) and CloudSat measurements. For this purpose, we create a new dataset called the Cumulus And Stratocumulus CloudSat-CALIPSO Dataset (CASCCAD), which identifies Sc, broken Sc, Cu under Sc, Cu with stratiform outflow and Cu. To separate the Cu from Sc, we design an original method based on the cloud height, horizontal extent, vertical variability and horizontal continuity, which is separately applied to both CALIPSO and combined CloudSatCALIPSO observations. First, the choice of parameters used in the discrimination algorithm is investigated and validated in selected Cu, Sc and ScCu transition case studies. Then, the global statistics are compared against those from existing passive- and active-sensor satellite observations. Our results indicate that the cloud optical thickness as used in passive-sensor observations is not a sufficient parameter to discriminate Cu from Sc clouds, in agreement with previous literature. Using clustering-derived datasets shows better results although one cannot completely separate cloud types with such an approach. On the contrary, classifying Cu and Sc clouds and the transition between them based on their geometrical shape and spatial heterogeneity leads to spatial distributions consistent with prior knowledge of these clouds, from ground-based, ship-based and field campaigns. Furthermore, we show that our method improves existing ScCu classifications by using additional information on cloud height and vertical cloud fraction variation. Finally, the CASCCAD datasets provide a basis to evaluate shallow convection and stratocumulus clouds on a global scale in climate models and potentially improve our understanding of low-level cloud feedbacks. The CASCCAD dataset (Cesana, 2019, https://doi.org/10.5281/zenodo.2667637) is available on the Goddard Institute for Space Studies (GISS) website at https://data.giss.nasa.gov/clouds/casccad/ (last access: 5 November 2019) and on the zenodo website at https://zenodo.org/record/2667637 (last access: 5 November 2019)

Cumulo: A Dataset for Learning Cloud Classes

One of the greatest sources of uncertainty in future climate projections comes from limitations in modelling clouds and in understanding how different cloud types interact with the climate system. A key first step in reducing this uncertainty is to accurately classify cloud types at high spatial and temporal resolution. In this paper, we introduce Cumulo, a benchmark dataset for training and evaluating global cloud classification models. It consists of one year of 1km resolution MODIS hyperspectral imagery merged with pixel-width 'tracks' of CloudSat cloud labels. Bringing these complementary datasets together is a crucial first step, enabling the Machine-Learning community to develop innovative new techniques which could greatly benefit the Climate community. To showcase Cumulo, we provide baseline performance analysis using an invertible flow generative model (IResNet), which further allows us to discover new sub-classes for a given cloud class by exploring the latent space. To compare methods, we introduce a set of evaluation criteria, to identify models that are not only accurate, but also physically-realistic. CUMULO can be download from https://www.dropbox.com/sh/6gca7f0mb3b0ikz/AADq2lk4u7k961Qa31FwIDEpa?dl=0

Cloud type comparisons of AIRS, CloudSat, and CALIPSO cloud height and amount

The precision of the two-layer cloud height fields derived from the Atmospheric Infrared Sounder (AIRS) is explored and quantified for a five-day set of observations. Coincident profiles of vertical cloud structure by CloudSat, a 94 GHz profiling radar, and the Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observation (CALIPSO), are compared to AIRS for a wide range of cloud types. Bias and variability in cloud height differences are shown to have dependence on cloud type, height, and amount, as well as whether CloudSat or CALIPSO is used as the comparison standard. The CloudSat-AIRS biases and variability range from −4.3 to 0.5±1.2–3.6 km for all cloud types. Likewise, the CALIPSO-AIRS biases range from 0.6–3.0±1.2–3.6 km (−5.8 to −0.2±0.5–2.7 km) for clouds ≥7 km (<7 km). The upper layer of AIRS has the greatest sensitivity to Altocumulus, Altostratus, Cirrus, Cumulonimbus, and Nimbostratus, whereas the lower layer has the greatest sensitivity to Cumulus and Stratocumulus. Although the bias and variability generally decrease with increasing cloud amount, the ability of AIRS to constrain cloud occurrence, height, and amount is demonstrated across all cloud types for many geophysical conditions. In particular, skill is demonstrated for thin Cirrus, as well as some Cumulus and Stratocumulus, cloud types infrared sounders typically struggle to quantify. Furthermore, some improvements in the AIRS Version 5 operational retrieval algorithm are demonstrated. However, limitations in AIRS cloud retrievals are also revealed, including the existence of spurious Cirrus near the tropopause and low cloud layers within Cumulonimbus and Nimbostratus clouds. Likely causes of spurious clouds are identified and the potential for further improvement is discussed

A 3D cloud-construction algorithm for the EarthCARE satellite mission

This article presents and assesses an algorithm that constructs 3D distributions of cloud from passive satellite imagery and collocated 2D nadir profiles of cloud properties inferred synergistically from lidar, cloud radar and imager data. It effectively widens the active–passive retrieved cross-section (RXS) of cloud properties, thereby enabling computation of radiative fluxes and radiances that can be compared with measured values in an attempt to perform radiative closure experiments that aim to assess the RXS. For this introductory study, A-train data were used to verify the scene-construction algorithm and only 1D radiative transfer calculations were performed. The construction algorithm fills off-RXS recipient pixels by computing sums of squared differences (a cost function F) between their spectral radiances and those of potential donor pixels/columns on the RXS. Of the RXS pixels with F lower than a certain value, the one with the smallest Euclidean distance to the recipient pixel is designated as the donor, and its retrieved cloud properties and other attributes such as 1D radiative heating rates are consigned to the recipient. It is shown that both the RXS itself and Moderate Resolution Imaging Spectroradiometer (MODIS) imagery can be reconstructed extremely well using just visible and thermal infrared channels. Suitable donors usually lie within 10 km of the recipient. RXSs and their associated radiative heating profiles are reconstructed best for extensive planar clouds and less reliably for broken convective clouds. Domain-average 1D broadband radiative fluxes at the top of theatmosphere(TOA)for (21 km)2 domains constructed from MODIS, CloudSat andCloud–Aerosol Lidar and Infrared Pathfinder Satellite Observations (CALIPSO) data agree well with coincidental values derived from Clouds and the Earth’s Radiant Energy System (CERES) radiances: differences betweenmodelled and measured reflected shortwave fluxes are within±10Wm−2 for∼35% of the several hundred domains constructed for eight orbits. Correspondingly, for outgoing longwave radiation∼65% are within ±10Wm−2

Interaction between aerosols and the mesoscale convective systems over the tropical continents

Presence of aerosols in the upper troposphere can have significant impacts on the Earth’s radiative energy budget. However, the aerosol–cloud relationship represents the largest uncertainty in the radiative energy budget. Relationships between aerosols and the mesoscale convective systems (MCSs) are complicated and difficult to ascertain, due in large part to inadequate availability of satellite datasets until recent years. Variation of aerosol impacts with meteorological parameters and the relative influence of these parameters on the convective strength of the MSCs can also be attributed to limited detectability of aerosol invigoration effects. To address the interaction between aerosol and the MCSs, I first address the influence of MCS on the distribution of the aerosols, which is poorly known on a global scale. Then, I investigate the influence of aerosol on MCSs. This dissertation addresses these problems by collocating a suite of geostationary and polar orbital satellites at three different phases of their convective lifecycle. First, I estimate the extent of upper tropospheric aerosol layers (UT ALs) surrounding the MCSs and explore the relationships between UT AL extent and the morphology, location, and developmental stages of collocated MCSs in the tropics over equatorial Africa, South Asia, and the Amazon basin between June 2006 and June 2008. I identify that the most extensive UT ALs over equatorial Africa are associated with the mature MCSs, while the most extensive UT ALs over South Asia and the Amazon basin are associated with the growing MCSs. Convective aerosol transport over Amazonia is weaker than that observed over the other two regions despite similar transport frequencies, likely due to smaller sizes and shorter mean lifetimes of Amazonian MCSs. Variations in UT ALs in the vicinity of tropical MCSs are primarily explained by variations in the horizontal sizes of the associated MCSs and are not related to aerosol loading in the lower troposphere. Relationships between convective properties and aerosol transport are relatively weak during the decaying stage of convective development. Then I estimate the relative influence of aerosols and other meteorological parameters on MCS strength and longevity using collocated samples of MCSs from January 2003 to June 2008. The results show that relative humidity (RH) and convective available potential energy (CAPE) have the strongest impacts on MCS lifetime and enhance the lifetime of the MCSs by 6-36 hours when other parameters such as vertical wind shear (VWS) and aerosols are kept constant. Aerosols also enhance the convective lifetime of MCSs, however at a much weaker rate (6-24h) and only when RH and VWS are high. Moreover, aerosol influence on convective lifetime is detected during the mature and decaying phases only. At the continental scale, aerosols explain 20-27% of the total variance of MCSs’ lifetime over equatorial South America, but explain only 8% of the same over equatorial Africa. South Asian MCSs are more strongly influenced by meteorological parameters and MCS-associated aerosols when they are over the ocean than when over the land since most MCSs form and develop over the oceans. After that, I estimate the influence of aerosols and other meteorological parameters on MCSs’ rain rate (RR). Results show that an increase in aerosol concentration enhance IWC and suppress RR and LH during all three phases of convective lifetime. Increasing aerosol concentrations suppress RR at the rate of -0.38 mm/h and -0.47 mm/h during the growing, decaying phases when VWS is high and at a rate of -0.30 mm/h during the mature phase when RH is low. Meteorological parameters such as VWS and RH have significant effects on these aerosol influences. The suppression of RR is also associated with a decrease in latent heat released by large hydrometeors. Aerosols explain 16%, 23%, and 29% of RR’s variance during the growing, mature and decaying phases, respectively, as estimated by a multiple linear regression method. Consequently, aerosols enhance IWC of the MCSs inside the anvil up to 0.72, 1.41, 0.82 mg/m3 and enhance the total integrated reflectivity of the larger-sized ice particles up to 8, 11, and 18 dBZ in the convective core regions during the growing, mature and decaying phases, respectively. In contrast, changes (one standard deviation) in CAPE and RH enhance the RR up to 0.35 mm/h. This dissertation study provides the first satellite based global tropical assessment of the relative influences of aerosols and meteorological conditions on MCSs’ lifetime, rain rate, and IWC and the mutual dependence of these influences. It also shows how aerosols influence the rain rate, cloud ice and lifetime of the MCSs, varying within their lifecycle and between different tropical continents ranging from humid equatorial South America during wet season and big monsoonal systems over South Asia to relatively dry equatorial Africa with high aerosol loading. In doing so, this work has also advanced our capability to evaluate whether or not aerosols could increase convective lifetime by suppressing rain rate and invigorating the MCSs on climate scale and what are the favorable meteorological conditions for aerosol to affect the lifetime of the MCSs. Our results also provide an interpretive framework for devising and evaluating numerical model experiments that can examine relationships between convective properties and ALs transported in the upper troposphere. In the future, we would like to investigate the influence of different meteorological parameters and aerosols on extra tropical MCSs and on self-aggregation of convection.Geological Science

A Feedforward Neural Network Approach for the Detection of Optically Thin Cirrus From IASI-NG

The identification of optically thin cirrus is crucial for their accurate parameterization in climate and Earth's system models. This study exploits the characteristics of the infrared atmospheric sounding interferometer-new generation (IASI-NG) to develop an algorithm for the detection of optically thin cirrus. IASI-NG has been designed for the European Organization for the Exploitation of Meteorological Satellites (EUMETSAT) polar system second-generation program to continue the service of its predecessor IASI from 2024 onward. A thin-cirrus detection algorithm (TCDA) is presented here, as developed for IASI-NG, but also in parallel for IASI to evaluate its performance on currently available real observations. TCDA uses a feedforward neural network (NN) approach to detect thin cirrus eventually misidentified as clear sky by a previously applied cloud detection algorithm. TCDA also estimates the uncertainty of "clear-sky" or "thin-cirrus" detection. NN is trained and tested on a dataset of IASI-NG (or IASI) simulations obtained by processing ECMWF 5-generation reanalysis (ERA5) data with the s-IASI radiative transfer model. TCDA validation against an independent simulated dataset provides a quantitative statistical assessment of the improvements brought by IASI-NG with respect to IASI. In fact, IASI-NG TCDA outperforms IASI TCDA by 3% in probability of detection (POD), 1% in bias, and 2% in accuracy, and the false alarm ratio (FAR) passes from 0.02 to 0.01. Moreover, IASI TCDA validation against state-of-the-art cloud products from Cloudsat/CPR and CALIPSO/Cloud-Aerosol Lidar with Orthogonal Polarization (CALIOP) real observations reveals a tendency for IASI TCDA to underestimate the presence of thin cirrus (POD = 0.47) but with a low FAR (0.07), which drops to 0.0 for very thin cirrus

Spatio-temporal variability of warm rain events over southern West Africa from geostationary satellite observations for climate monitoring and model evaluation

This paper presents the spatiotemporal variability of warm rain events over southern West Africa (SWA) during the summer monsoon season for the first time, using Spinning Enhanced Visible Infrared Radiometer (SEVIRI) observations on the Meteosat geostationary satellites. The delineation of warm rain events is based on the principle that precipitating low-level clouds are associated with either sufficient water content or large cloud droplet size. Capitalising on the ability of spaceborne radar to resolve vertical cloud structures and detect the presence of precipitation, the delineation is trained by collocated SEVIRI and CloudSat observations. The resulting 12-years of observations from SEVIRI are used to examine the spatial, diurnal, seasonal and interannual variability of warm rain events over SWA. Warm rain events predominate during the monsoon in August, with little interannual variability, and persist over orography in the morning and the coasts after midday, likely enhanced by orographic lifting and land-sea breeze effects. Warm clouds have a much higher probability of precipitation along the coastlines of Liberia and Nigeria compared to the central SWA coastline and further inland. Finally, when evaluating an 8-day yet high-spatial resolution model simulation, we find that warm rain frequencies from the simulation agree well with SEVIRI near the coast but simulated warm cloud cover and thus warm rain frequencies are too low over the Gulf of Guinea. The probability of precipitation of warm clouds is also too low inland. The newly developed climatology creates opportunities to further investigate the diurnal cycle of warm rain, study aerosol-cloud-precipitation interactions, and assess the role of warm rain in the water cycle across Africa and beyond

Evaluation of NCEP GFS cloud properties using satellite retrievals and ground-based measurements

Cloud properties and their vertical structure are important for meteorological studies due to their impact on both the Earth's radiation budget and adiabatic heating. Examination of bulk cloud properties and vertical distribution simulated by the National Center for Environmental Prediction (NCEP) Global Forecast System (GFS) using various satellite products and ground-based measurements is a main objective of this study. Cloud variables evaluated include the occurrence and fraction of clouds in three layers, cloud optical depth, liquid water path, and ice water path. Cloud vertical structure data are retrieved from both active and passive sensors that are compared with GFS model results. In general, the GFS model captures the spatial patterns of hydrometeors reasonably well and follows the general features seen in satellite measurements, but large discrepancies exist in low-level cloud properties. More boundary layer clouds over the interior continents were generated by the GFS model whereas satellite retrievals showed more low-level clouds over oceans. The GFS model simulations also missed low, shallow stratocumulus clouds along the west coast of North America, South America, and southwestern Africa and overestimated thick, large-scale clouds associated with the Asian summer monsoon. Although the frequencies of global multi-layer clouds from observations are similar to those from the model, latitudinal variations show large discrepancies in terms of structure and pattern. The modeled cloud optical depth for optically thin or intermediate clouds is less than that from passive sensor and is overestimated for optically thick clouds. The distributions of ice water path (IWP) agree better with satellite observations than do liquid water path (LWP) distributions. Mistreatment of such stratocumulus clouds in the GFS model leads to an overestimation of upward longwave flux, and an underestimation of upward shortwave flux at the top-of-atmosphere (TOA). With respect to input data bias in cloud fields, the GFS temperature is comparable with satellite retrievals and ground-based measurements, but the GFS relative humidity shows a wet bias at 150 and 850 hPa both from satellite retrievals and ground-based measurements. Discrepancies in cloud fields between observations and the model are attributed to differences in cloud water mixing ratio and mean relative humidity fields, which are major control variables determining the formation of clouds. To improve the simulation of cloud fields, application of other cloud parameterization scheme to the GFS model is performed. The new scheme generates a large quantity of marine stratocumulus clouds over the eastern tropical oceans as well as low cloud amounts in the other regions. High-level and middle-level clouds generated from the new scheme are more comparable with the satellite retrievals in terms of the spatial distributions and zonally averaged cloud fractions. An application of a simple linear relationship between de-correlation lengths (Lcf) and latitudes to the GFS model is conducted in order to see how successfully the equation explains the characteristics of cloud vertical structure on the changes in cloud fraction at different vertical levels. The method to solve for Lcf is a combination of Brent (1973) approach and a stochastic cloud generator using data collected from space-borne active sensors. Cloud fractions derived from a simple linear fit are compared to those computed from Lcf values based on observations. The pattern of zonal Lcf values from a simple linear fit is quite different from that of Lcf values based on observations. An offset pattern in subtropical regions is notable. The distribution of median Lcf values calculated from observed clouds do not show much dependence on latitude. This suggests that other physics, such as convection and cloud formation mechanism rather than simply latitude, should be considered when explaining how Lcf behaves. Such findings are expected to help improve the inherent problems of the GFS cloud parameterization scheme and to gain insight into the method used in determining cloud fraction