Path-tracing Monte Carlo Library for 3D Radiative Transfer in Highly Resolved Cloudy Atmospheres

Interactions between clouds and radiation are at the root of many difficulties in numerically predicting future weather and climate and in retrieving the state of the atmosphere from remote sensing observations. The large range of issues related to these interactions, and in particular to three-dimensional interactions, motivated the development of accurate radiative tools able to compute all types of radiative metrics, from monochromatic, local and directional observables, to integrated energetic quantities. In the continuity of this community effort, we propose here an open-source library for general use in Monte Carlo algorithms. This library is devoted to the acceleration of path-tracing in complex data, typically high-resolution large-domain grounds and clouds. The main algorithmic advances embedded in the library are those related to the construction and traversal of hierarchical grids accelerating the tracing of paths through heterogeneous fields in null-collision (maximum cross-section) algorithms. We show that with these hierarchical grids, the computing time is only weakly sensitivive to the refinement of the volumetric data. The library is tested with a rendering algorithm that produces synthetic images of cloud radiances. Two other examples are given as illustrations, that are respectively used to analyse the transmission of solar radiation under a cloud together with its sensitivity to an optical parameter, and to assess a parametrization of 3D radiative effects of clouds.Comment: Submitted to JAMES, revised and submitted again (this is v2

Small and optically thin clouds in the trades

The trades and the inherent trade cumulus clouds cover large parts of the tropical oceans. Trade cumulus clouds are ubiquitous but also very small in their horizontal and vertical extent posing huge challenges on observing systems such as satellite imagers. Climate models exhibit a significant spread in the response of trade cumulus clouds to global warming motivating their intense study in recent years. Within this thesis, I use high-resolution satellite images to gain new insights on small and optically thin clouds in the trades. The way trade wind clouds change with surface warming is decisive for their feedback, which defines whether clouds further amplify or dampen the warming of the climate system. Cloud feedback estimates can be investigated from so-called cloud-controlling factors, their relation to cloud properties in the current climate and their change with global warming. Results from my first study indicate a wind-speed driven boundary layer in the trades. The surface trade winds show the most powerful control on cloud properties such as cloud sizes, top heights or cloud clustering. Furthermore, the Bowen ratio was firstly tested from observations and emerges as a potential new control factor. Trade cumulus cloud properties also show a susceptibility to the sea surface temperature and the stability of the lower troposphere which are both projected to change in a warming climate and may thus impact cloud feedbacks. Investigating cloud-controlling factors is an ongoing task and seems to be within reach from extensive measurements of the recent field campaign EUREC4A. First analysis of cloud observations from multiple instruments indicate the frequent occurrence of not only small, but also optically thin clouds. Due to their low reflectance, such clouds are challenging to detect from passive imagers. High- resolution imagers are able to detect small clouds, but, do conventional satellite cloud products still miss optically thin clouds? Within another study, I follow a new approach for defining the total cloud cover consisting of clouds detected by conventional cloud masking schemes and of undetected optically thin clouds. By simulating the well-understood clear-sky signal I can extract clouds as a residual from the all-sky observation and circumvent conventional but problematic thresholding tests in cloud masking schemes. From evaluating a high-resolution satellite dataset collected during EUREC4A, I find that optically thin clouds contribute 45 % to the total cloud cover and reduces the average cloud reflectance by 29 %. Undetected optically thin clouds can have major implications for estimates of the radiative effect of clouds and thus, cloud feedbacks

Visualizing Airborne and Satellite Imagery

Remote sensing is a process able to provide information about Earth to better understand Earth's processes and assist in monitoring Earth's resources. The Cloud Absorption Radiometer (CAR) is one remote sensing instrument dedicated to the cause of collecting data on anthropogenic influences on Earth as well as assisting scientists in understanding land-surface and atmospheric interactions. Landsat is a satellite program dedicated to collecting repetitive coverage of the continental Earth surfaces in seven regions of the electromagnetic spectrum. Combining these two aircraft and satellite remote sensing instruments will provide a detailed and comprehensive data collection able to provide influential information and improve predictions of changes in the future. This project acquired, interpreted, and created composite images from satellite data acquired from Landsat 4-5 Thematic Mapper (TM) and Landsat 7 Enhanced Thematic Mapper plus (ETM+). Landsat images were processed for areas covered by CAR during the Arctic Research of the Composition of the Troposphere from Aircraft and Satellites (ARCT AS), Cloud and Land Surface Interaction Campaign (CLASIC), Intercontinental Chemical Transport Experiment-Phase B (INTEXB), and Southern African Regional Science Initiative (SAFARI) 2000 missions. The acquisition of Landsat data will provide supplemental information to assist in visualizing and interpreting airborne and satellite imagery

The analysis of polar clouds from AVHRR satellite data using pattern recognition techniques

The cloud cover in a set of summertime and wintertime AVHRR data from the Arctic and Antarctic regions was analyzed using a pattern recognition algorithm. The data were collected by the NOAA-7 satellite on 6 to 13 Jan. and 1 to 7 Jul. 1984 between 60 deg and 90 deg north and south latitude in 5 spectral channels, at the Global Area Coverage (GAC) resolution of approximately 4 km. This data embodied a Polar Cloud Pilot Data Set which was analyzed by a number of research groups as part of a polar cloud algorithm intercomparison study. This study was intended to determine whether the additional information contained in the AVHRR channels (beyond the standard visible and infrared bands on geostationary satellites) could be effectively utilized in cloud algorithms to resolve some of the cloud detection problems caused by low visible and thermal contrasts in the polar regions. The analysis described makes use of a pattern recognition algorithm which estimates the surface and cloud classification, cloud fraction, and surface and cloudy visible (channel 1) albedo and infrared (channel 4) brightness temperatures on a 2.5 x 2.5 deg latitude-longitude grid. In each grid box several spectral and textural features were computed from the calibrated pixel values in the multispectral imagery, then used to classify the region into one of eighteen surface and/or cloud types using the maximum likelihood decision rule. A slightly different version of the algorithm was used for each season and hemisphere because of differences in categories and because of the lack of visible imagery during winter. The classification of the scene is used to specify the optimal AVHRR channel for separating clear and cloudy pixels using a hybrid histogram-spatial coherence method. This method estimates values for cloud fraction, clear and cloudy albedos and brightness temperatures in each grid box. The choice of a class-dependent AVHRR channel allows for better separation of clear and cloudy pixels than does a global choice of a visible and/or infrared threshold. The classification also prevents erroneous estimates of large fractional cloudiness in areas of cloudfree snow and sea ice. The hybrid histogram-spatial coherence technique and the advantages of first classifying a scene in the polar regions are detailed. The complete Polar Cloud Pilot Data Set was analyzed and the results are presented and discussed

Meso-scale patterns of shallow convection in the trades

How will marine low-level cloudiness change in a warming climate? To answer this ques- tion a better process understanding of low-level cloudiness is needed. This dissertation uses a multitude of observations and large-eddy simulations to explore how meso-scale patterns of shallow convection relate to this challenging question. This study focuses on the downwind trades and its meso-scale patterns that only recently raised interest based on the work of Stevens et al. (2020) who supplemented the traditional classes of meso-scale patterns of the upstream trades. These new classes are named based on their visual impression Sugar, Gravel, Flowers and Fish. Here they are further investigated in terms of their climatic relevance, physical characteristics, atmospheric environment and emergence. The core of these investigations consists of deep neural networks that have been trained to identify these patterns in satellite images. At the same time, the deep neural networks proved to be a valuable tool to derive a common perception of subjectively defined classes that do not have a ground truth. Although the crowd-sourced labels were therefore very noisy, the neural networks ranked among the highest in inter-annotator agreements. The classification of the neural network reveals that the patterns are common to the trades beyond the winter season in the western North Atlantic and can represent more than 40 % of the observed variability depending on season and region. This variability expresses itself not only in changes of the visual appearance but also physically in the cloud cover. A linear relationship between the cloud cover and the cloud radiative effect makes the processes leading to the patterns relevant for the climate. The underlying physical processes of each meso-scale pattern are related to the air- mass origin with an influence of diurnal variations that are potentially modulating the large-scale factors. One large-scale factor that is most distinct among the patterns is wind speed. Other factors are only related to a particular pattern but can be a necessity for the pattern to form. Fish for example is associated with anomalously strong convergence. Sugar favors warmer surface temperatures. Both the forcing of Fish and Sugar are related to air-masses intruding from outside the trades, leaving Gravel and Flowers be the only native trade-wind patterns. Large-eddy simulations reveal that they are in general capable of replicating the observed variability in meso-scale cloud patterns. However, they are unable to match the observed vertical distribution of cloudiness in both their absolute values and their variability in particular for Flowers and Fish. Nevertheless, the distribution of moisture and the presence of meso-scale circulations indicates that the responsible processes for the formation of the different patterns are captured and the simulations are a valuable tool to complement the observations to gain a better process understanding. Based on the relationships between large-scale forcing and mesoscale patterns found in this dissertation, conditions preferred by patterns with higher cloud amount and more negative cloud radiative effect are expected to occur less frequently in a warming climate

Estimation of the instantaneous downward surface shortwave radiation using MODIS data in Lhasa for all-sky conditions

Measuring the solar irradiance with high accuracy is the basis of PV power forecasting. Although the downward surface shortwave radiation (DSSR) data derived from satellite images are widely used in the PV industry, the instantaneity and accuracy of these data are not suitable for PV power forecasting in a short-time period. In this study, an algorithm to calculate instantaneous DSSR for all-sky conditions was developed by combining clear-sky radiative transfer model and 3D radiative transfer model using MODIS products (MOD03-07, 09). The algorithm was evaluated by ground measurements from a station in Lhasa and a reference dataset from FLASHFlux. The results indicate that the errors of DSSR using combining model are less than FLASHFlux. The time consuming of running 3D radiative transfer model can be reduced by narrowing down the extent of input data to 8km

Investigation of mesoscale cloud features viewed by LANDSAT

The author has identified the following significant results. Some 50 LANDSAT images displaying mesoscale cloud features were analyzed. This analysis was based on the Rayleigh-Kuettner model describing the formation of that type of mesoscale cloud feature. This model lends itself to computation of the average wind speed in northerly flow from the dimensions of the cloud band configurations measured from a LANDSAT image. In nearly every case, necessary conditions of a curved wind profile and orientation of the cloud streets within 20 degrees of the direction of the mean wind in the convective layer were met. Verification of the results by direct observation was hampered, however, by the incompatibility of the resolution of conventional rawinsonde observations with the scale of the banded cloud patterns measured from LANDSAT data. Comparison seems to be somewhat better in northerly flows than in southerly flows, with the largest discrepancies in wind speed being within 8m/sec, or a factor of two

On modeled and observed warm rainfall occurrence and its relationships with cloud macrophysical properties

2014 Spring.Includes bibliographical references.Rainfall from low-level, liquid-phase ("warm") clouds over the global oceans is ubiquitous and contributes non-negligibly to the total amount of precipitation that falls to the globe. In this study, modeled and observed warm rainfall occurrence and its bulk statistical relationships with cloud macrophysical properties are analyzed independently and directly compared with one another. Rain is found to fall from ~25% of the warm, maritime clouds observed from space by CloudSat and from ~27% of the warm clouds simulated within a large-scale, fine-resolution radiative convective equilibrium experiment performed with the Regional Atmospheric Modeling System (RAMS). Within both the model and the observations, the fractional occurrence of warm rainfall is found to increase with both column-integrated liquid water mass and cloud geometric depth, two cloud-scale properties that are shown to be directly related to one another. However, warm rain within RAMS is more likely with lower amounts of column water mass than observations indicate, suggesting that the parameterized cloud-to-rain conversion processes within RAMS produce rainfall too efficiently. To gain insight into the relationships between warm rainfall production and the concentration of liquid water within a cloud layer, warm rainfall occurrence is subsequently investigated as a joint, simultaneous function of both cloud depth and column-integrated water mass. While rainfall production within RAMS is largely governed by the availability of liquid water within the cloud volume, rain from observed warm clouds with relatively little column water mass is actually more likely to fall from deeper clouds with lower cloud-mean water contents. The latter, CloudSat-derived trend is shown to be robust across different seasons and environmental conditions; it varies little when the warm cloud distribution is stratified into ascending (day) and descending (night) CloudSat overpass groups. Using temperature differences between RAMS cloud tops and their immediate, surrounding environments as a proxy for cloud-top buoyancy, an attempt is then made to quantitatively investigate simulated warm rain occurrence within the broader context of cloud life cycle. It is found that rainfall likelihoods from RAMS-simulated warm clouds with cloud top temperatures warmer than their surrounding environments more closely resemble the overall CloudSat-derived rainfall occurrence trends. This result suggests that the CloudSat-observed warm cloud distribution is characterized by increased numbers of positively buoyant, developing clouds