Modeling the High-resolution Emission Spectra of Clear and Cloudy Nontransiting Hot Jupiters

The advent of high-resolution spectroscopy (R ≳ 25,000) as a method for characterization of exoplanet atmospheres has expanded our capability to study nontransiting planets, vastly increasing the number of planets accessible for observation. Many of the most favorable targets for atmospheric characterization are hot Jupiters, where we expect large spatial variation in physical conditions such as temperature, wind speed, and cloud coverage, making viewing geometry important. Three-dimensional models have generally simulated observational properties of hot Jupiters assuming edge-on viewing, which can be compared to observations of transiting planets, but neglected the large fraction of planets without nearly edge-on orbits. As the first investigation of how orbital inclination manifests in high-resolution emission spectra from three-dimensional models, we use a general circulation model to simulate the atmospheric structure of Upsilon Andromedae b, a typical nontransiting hot Jupiter with high observational interest, due the brightness of its host star. We compare models with and without clouds, and find that cloud coverage intensifies spatial variations by making colder regions dimmer and relatedly enhancing emission from the clear, hotter regions. This increases both the net Doppler shifts and the variation of the continuum flux amplitude over the course of the planet’s orbit. In order to accurately capture scattering from clouds, we implement a generalized two-stream radiative transfer routine for inhomogeneous multiple scattering atmospheres. As orbital inclination decreases, four key features of the high-resolution emission spectra also decrease in both the clear and cloudy models: (1) the average continuum flux level, (2) the amplitude of the variation in continuum with orbital phase, (3) net Doppler shifts of spectral lines, and (4) Doppler broadening in the spectra. Models capable of treating inhomogeneous cloud coverage and different viewing geometries are critical in understanding results from high-resolution emission spectra, enabling an additional avenue to investigate these extreme atmospheres.

Intensity of land use by pasture as result of pedogeoclimatic characteristics and anthropogenic management

In the world, the most significant change in the ecosystems structure is the conversion from natural land surface into cultivated systems. In 2018, 26.8% of the Brazilian territory was occupied by agricultural activities, from which 73% is pasture. Considering that the management adopted in Brazilian pastures is incipient and leads to degradation, there is a need to characterize the state of the pastures to diagnose the intensity of this use on the soil. However, the diagnosis of large areas using satellites with more detailed resolution is limited by cloud coverage and low temporal resolution. In this sense, the present work aims to diagnose the intensity of land use by pastures (ILUP) in large areas based on the mosaic of images from Landsat 8 (LS8), Landsat 7 (LS7), Sentinel-2 (S2), and MODIS. The methodology consists of harmonizing the NDVI from LS7 and S2 satellites with LS8. For MODIS, the harmonization was carried out based on ILUP obtained previously from NDVI LS8. The methodology was applied at the Doce river basin (DRB). The combination of different sensors allowed to overcome the cloud coverage limitation. DRB has 61.3% of its area occupied by pastures and 78.2% of them have some degree of degradation. ILUP was dependent on DRB’s pedological and climatic characteristics. This dependence is enhanced due to pasture management in the basin, mainly characterized by continuous grazing, which commonly leads to overgrazing scenarios. The areas with great rainfall seasonality and associated with Acrisols/Cambisols are the most susceptible to degradation.

Improving Urban Land Cover Classification in Cloud-Prone Areas with Polarimetric SAR Images

Urban land cover (ULC) serves as fundamental environmental information for urban studies, while accurate and timely ULC mapping remains challenging due to cloud contamination in tropical and subtropical areas. Synthetic aperture radar (SAR) has excellent all-weather working capability to overcome the challenge, while optical SAR data fusion is often required due to the limited land surface information provided by SAR. However, the mechanism by which SAR can compensate optical images, given the occurrence of clouds, in order to improve the ULC mapping, remains unexplored. To address the issue, this study proposes a framework, through various sampling strategies and three typical supervised classification methods, to quantify the ULC classification accuracy using optical and SAR data with various cloud levels. The land cover confusions were investigated in detail to understand the role of SAR in distinguishing land cover under different types of cloud coverage. Several interesting experimental results were found. First, 50% cloud coverage over the optical images decreased the overall accuracy by 10–20%, while the incorporation of SAR images was able to improve the overall accuracy by approximately 4%, by increasing the recognition of cloud-covered ULC information, particularly the water bodies. Second, if all the training samples were not contaminated by clouds, the cloud coverage had a higher impact with a reduction of 35% in the overall accuracy, whereas the incorporation of SAR data contributed to an increase of approximately 5%. Third, the thickness of clouds also brought about different impacts on the results, with an approximately 10% higher reduction from thick clouds compared with that from thin clouds, indicating that certain spectral information might still be available in the areas covered by thin clouds. These findings provide useful references for the accurate monitoring of ULC over cloud-prone areas, such as tropical and subtropical cities, where cloud contamination is often unavoidable.

Variability of Diffuse Daylight Due to the Diversity of Cloud Arrays

Illuminance modeling that allows us to mimic or even replicate the dynamics of daylight changes is increasingly becoming a challenge for the development of more accurate prediction systems of natural light availability in building interiors or variations of insolation at arbitrarily oriented façades. We demonstrate that illuminance amplitude due to random cloud arrangement can vary over a wide range even when other atmospheric parameters remain unchanged. It follows from our systematic numerical modeling that diffuse illuminance predictions can be significantly improved by incorporating cloud coverage, mean cloud size, and cloud base altitude into daylight models. We show that any model of homogeneous luminance patterns would fail in modeling the illuminance amplitudes we can expect on horizontal and vertical planes under partial cloud coverage with individual clouds distributed randomly. However, these situations occur with high frequency in most of regions worldwide, thus the modeling results we obtained here are of high relevance to daylight modeling and solar energy systems as well.

Global trends in downward surface solar radiation from spatial interpolated ground observations during 1961-2019

Downward surface solar radiation (SSR) is a crucial component of the Global Energy Balance, affecting temperature and the hydrological cycle profoundly, and it provides crucial information about climate change. Many studies have examined SSR trends, however they are often concentrated on specific regions due to limited spatial coverage of ground based observation stations. To overcome this spatial limitation, this study performs a spatial interpolation based on a machine learning method, Random Forest, to interpolate monthly SSR anomalies using a number of climatic variables (various temperature indices, cloud coverage, etc.), time point indicators (years and months of SSR observations), and geographical characteristics of locations (latitudes, longitudes, etc). The predictors that provide the largest explanatory power for interannual variability are diurnal temperature range and cloud coverage. The output of the spatial interpolation is a 0:5° ×0:5° monthly gridded dataset of SSR anomalies with complete land coverage over the period 1961-2019, which is used afterwards in a comprehensive trend analysis for i) each continent separately, and ii) the entire globe.The continental level analysis reveals the major contributors to the global dimming and brightening. In particular, the global dimming before the 1980s is primarily dominated by negative trends in Asia and North America, while Europe and Oceania have been the two largest contributors to the brightening after 1982 and up until 2019.

Adapting the COSP Radar Simulator to Compare GCM Output and GPM Precipitation Radar Observations

Comparisons of precipitation between general circulation models (GCMs) and observations are often confounded by a mismatch between model output and instrument measurements, including variable type and temporal and spatial resolution. To mitigate these differences, the radar-simulator Quickbeam within the Cloud Feedback Model Intercomparison Project (CFMIP) Observation Simulator Package (COSP) simulates reflectivity from model variables at the sub-grid scale. This work adapts Quickbeam to the dual-frequency Precipitation Radar (DPR) onboard the Global Precipitation Measurement (GPM) satellite. The longer wavelength of the DPR is used to evaluate moderate-to-heavy precipitation in GCMs, which is missed when Quickbeam is used as a cloud radar simulator. Latitudinal and land/ocean comparisons are made between COSP output from the Community Atmospheric Model version 5 (CAM5) and DPR data. Additionally, this work improves the COSP sub-grid algorithm by applying a more realistic, non-deterministic approach to assigning GCM grid box convective cloud cover when convective cloud is not provided as a model output. Instead of assuming a static 5% convective cloud coverage, DPR convective precipitation coverage is used as a proxy for convective cloud coverage. For example, DPR observations show that convective rain typically only covers about 1% of a 2° grid box, but that the median convective rain area increases to over 10% in heavy rain cases. In our CAM5 tests, the updated sub-grid algorithm improved the comparison between reflectivity distributions when the convective cloud cover is provided versus the default 5% convective cloud cover assumption.

The Influence of Wildfire Smoke on Cloud Microphysics during the September 2020 Pacific Northwest Wildfires

During late summer 2020, large wildfires over the Pacific Northwest produced dense smoke that impacted the region for an extended period. During this period of poor air quality, persistent low-level cloud coverage was poorly forecast by operational numerical weather prediction models, which dissipated clouds too quickly or produced insufficient cloud coverage extent. This deficiency raises questions about the influence of wildfire smoke on low-level clouds in the marine environment of the Pacific Northwest.This paper investigates the effects of wildfire smoke on the properties of low-level clouds, including their formation, microphysical properties, and dissipation. A case study from 12-14 September 2020 is used as a testbed to evaluate the impact of wildfire smoke on such clouds. Observations from satellites and surface observing sites, coupled with mesoscale model simulations, are applied to understand the influence of wildfire smoke during this event. Results indicate that the presence of thick smoke over Washington led to decreased temperatures in the lower troposphere which enhanced low-level cloud coverage, with smoke particles altering the microphysical structure of clouds to favor high concentrations of small droplets. Thermodynamic changes due to smoke are found to be the primary driver of enhanced cloud lifetime during these events, with microphysical changes to clouds as a secondary contributing factor. However, both the thermodynamic and microphysical effects are necessary to produce a realistic simulation.