Cloud-free Days Research Articles

Novel Learning of Bathymetry from Landsat 9 Imagery Using Machine Learning, Feature Extraction and Meta-Heuristic Optimization in a Shallow Turbid Lagoon

Bathymetry data is indispensable for a variety of aquatic field studies and benthic resource inventories. Determining water depth can be accomplished through an echo sounding system or remote estimation utilizing space-borne and air-borne data across diverse environments, such as lakes, rivers, seas, or lagoons. Despite being a common option for bathymetry mapping, the use of satellite imagery faces challenges due to the complex inherent optical properties of water bodies (e.g., turbid water), satellite spatial resolution limitations, and constraints in the performance of retrieval models. This study focuses on advancing the remote sensing based method by harnessing the non-linear learning capabilities of the machine learning (ML) model, employing advanced feature selection through a meta-heuristic algorithm, and using image extraction techniques (i.e., band ratio, gray scale morphological operation, and morphological multi-scale decomposition). Herein, we validate the predictive capabilities of six ML models: Random Forest (RF), Support Vector Machine (SVM), CatBoost (CB), Extreme Gradient Boost (XGB), Light Gradient Boosting Machine (LGBM), and KTBoost (KTB) models, both with and without the application of meta-heuristic optimization (i.e., Dragon Fly, Particle Swarm Optimization, and Grey Wolf Optimization), to accurately ascertain water depth. This is achieved using a diverse input dataset derived from multi-spectral Landsat 9 imagery captured on a cloud-free day (19 September 2023) in a shallow, turbid lagoon. Our findings indicate the superior performance of LGBM coupled with Particle Swamp Optimization (R2 = 0.908, RMSE = 0.31 m), affirming the consistency and reliability of the feature extraction and selection-based framework, while offering novel insights into the expansion of bathymetric mapping in complex aquatic environments.

Thermal infrared shadow-hiding in GOES-R ABI imagery: snow and forest temperature observations from the SnowEx 2020 Grand Mesa field campaign

Abstract. The high temporal resolution of thermal infrared imagery from the Geostationary Operational Environmental Satellites R-series (GOES-R) presents an opportunity to observe mountain snow and forest temperatures over the full diurnal cycle. However, the off-nadir views of these imagers may impact or bias temperature observations, especially when viewing a surface composed of both snow and forests. We used GOES-16 and -17 thermal infrared brightness temperature observations of a flat snow- and forest-covered study site at Grand Mesa, Colorado, USA, to characterize how forest coverage and view angle impact these observations. These two geostationary satellites provided views of the study area from the southeast (134.1° azimuth, 33.5° elevation) and southwest (221.2° azimuth, 35.9° elevation), respectively. As part of the NASA SnowEx field campaign in February 2020, coincident brightness temperature observations from ground-based and airborne IR sensors were collected to compare with those from the geostationary satellites. Observations over the course of 2 cloud-free days spanned the entire study site. The brightness temperature observations from each dataset were compared to find their relative differences and how those differences may have varied over time and/or as a function of varying forest cover across the study area. GOES-16 and -17 brightness temperatures were found to match the diurnal cycle and temperature range within ∼ 1 h and ± 3 K of ground-based observations. GOES-16 and -17 were both biased warmer than nadir-looking airborne IR and ASTER observations. The warm biases were higher at times when the sun–satellite phase angle was near its daily minimum. The phase angle, the angle between the direction of incoming solar illumination and the direction from which the satellite is viewing, reached daily minimums in the morning for GOES-16 and afternoon for GOES-17. In morning observations, warm biases in GOES-16 brightness temperature were greater for pixels that contained more forest coverage. The observations suggest that a “thermal infrared shadow-hiding” effect may be occurring, where the geostationary satellites are preferentially seeing the warmer sunlit sides of trees at different times of day. These biases are important to understand for applications using GOES-R brightness temperatures or derived land surface temperatures (LSTs) over areas with surface roughness features, such as forests, that could exhibit a thermal infrared shadow-hiding effect.

Maize Evapotranspiration Estimates Using Planet Dove Mini-Satellites and Field-Level InfraRed Thermometers

Highlights Multispectral images from mini-satellites can be used in a reflectance-based crop coefficient model to estimate crop water use. Surface reflectance from mini-satellites and temperature from in-situ infrared thermometers can be used in a land surface energy balance model to more accurately estimate maize actual evapotranspiration. It is feasible to monitor daily maize actual evapotranspiration using Planet Dove mini-satellites on cloud-free days. Abstract. Water scarcity, climate change, and the need to sustain irrigated agriculture demand improved methods to manage irrigation water. Irrigation scheduling can be improved when remote sensing (RS) images are used because RS captures the actual crop field conditions. Mini-satellites (mS) are small spaceborne RS platforms that can provide high spatial and temporal resolution multispectral (MS) images. These multispectral images can be used to estimate maize actual evapotranspiration (ETa) on a daily basis. Thus, the main objective of this study was to assess the accuracy of maize ETa estimates when mS MS images were used in two different ETa algorithms. This study was conducted at two research sites in northern Colorado, USA. A reflectance-based crop coefficient and a one-source surface energy balance (OSSEB) models were applied, using MS Planet Dove images, to map maize ETa. An Eddy Covariance energy balance system was used to assess the accuracy of distributed ETa estimates. Results indicate that the reflectance-based crop coefficient method can be used with mS MS images yielding acceptable estimates. Both ETa models performed better in the case of the sub-surface irrigated maize. Further, OSSEB-based estimates of ETa model were more accurate over the surface irrigated maize and homogeneous soils; when surface reflectance images were paired with radiometric surface temperature data from ground-based infrared thermometers. These results imply that potentially maize ETa could be mapped, on a daily basis with good accuracy, using Planet Dove mS-based MS reflectance images and ground-based radiometric surface temperatures. Keywords: Agricultural water management, Crop water use, Irrigation, Land surface energy balance, Reflectance-based crop coefficients, Remote sensing.

Sensitivity of Banner Cloud Formation to Orography and the Ambient Atmosphere: Transition from Idealized to More Realistic Scenarios

Abstract Banner clouds are clouds in the lee of steep mountains or sharp ridges on otherwise cloud-free days. Previous studies investigated various aspects of banner cloud formation in numerical simulations, most of which were based on idealized orography and a neutrally stratified ambient atmosphere. The present study extends these simulations in two important directions by 1) examining the impact of various types of orography ranging from an idealized pyramid to the realistic orography of Mount Matterhorn and 2) accounting for an ambient atmosphere that turns from neutral to stably stratified below the mountain summit. Not surprisingly, realistic orography introduces asymmetries in the spanwise direction. At the same time, banner cloud occurrence remains associated with a coherent area of strong uplift, although this region does not have to be located exclusively in the lee of the mountain any longer. In the case of Mount Matterhorn with a westerly ambient flow, a large fraction of air parcels rises along the southern face of the mountain, before they reach the lee and are lifted into the banner cloud. The presence of a shallow boundary layer with its top below the mountain summit introduces more complex behavior compared to a neutrally stratified boundary layer; in particular, it introduces a dependence on wind speed, because strong wind is associated with strong turbulence that is able to raise the boundary layer height and, thus, facilitates the formation of a banner cloud.

A new approach to improve precipitable water vapour estimations of Sentinel-3A satellite data using LST, elevation and NDVI over Iran

ABSTRACT The total precipitable water vapour (TPW) extraction algorithm using the Sentinel-3A Ocean and Land Colour Instrument (OLCI) has the potential to be improved on a regional scale. The aim of this study was to improve the TPW extraction algorithm of OLCI for the first time using environmental variables, including the Normalized Difference Vegetation Index (NDVI), land surface temperature (LST), and elevation from mean sea level, on a regional scale over Iran. A previously developed TPW recovery algorithm was applied to the OLCI data during cloud-free days throughout a one-year period (1 January–29 December 2020). The artificial neural network (ANN) methodology was utilized in eight models. The evaluation results revealed the effectiveness of the models varied based on the topography and climate of each station. The assessment findings demonstrated that model 2, which integrated LST, elevation, and NDVI data in the ANN framework, outperformed other models across the study area.

A Method for Estimating Daytime Average Evapotranspiration From Diurnal Land Surface Temperature Measurements

Evapotranspiration (ET) is an essential parameter in the water cycle and surface energy balance. Accurate estimation of daytime or daily ET is of great significance for many activities in human economic and social development. This study aims to propose a novel method for estimating daytime average ET by using temporal measurements of land surface temperature (LST) and net surface shortwave radiation (NSSR), following a previous developed elliptical relationship between diurnal cycles of LST and NSSR under cloud-free days. The method was primarily developed from the simulated data of a physics-based Atmosphere–Land Exchange (ALEX) model under different underlying surfaces and atmospheric conditions. Based on the simulated data, the proposed method showed considerable accuracy with the overall coefficient of determination ( <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">R</i> <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> ) of 0.958 and the root mean square error (RMSE) of 25.3 Wm <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">-2</sup> . In addition, ground ET measurements at four Ameriflux sites (US-ARM, US-SRM, US-Whs, and US-Wkg) during the 2018 growing season were collected to assess the estimated daytime average ET. Results show an overall RMSE of 64.7 Wm <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">-2</sup> for the estimated ET at the four sites, and the US-Whs site reveals a best accuracy ( <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">R</i> <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> =0.825, RMSE=44.4 Wm <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">-2</sup> ). These results indicated a potential for generating daytime ET with geostationary satellite observations at regional scales in future development.

Relating atmospheric aerosol amounts to blue to red ratio and grayscale contrast fluctuations using digitalization of routine webcam photographs taken in the urban environment of Vienna

Visibility and visual contrast depend on several factors such as aerosol concentration, fog attenuation and humidity, viewing direction relative to the sun as well as trace gas amounts. Usually, visibility is determined by observers or by measurement devices such as visiometers. Only few studies reported on the relationship between visibility and aerosol concentration using digital cameras. In the present study, we develop and test new algorithms for the determination of the amount of aerosols by using digital photography. Two years of routine webcam recordings are used for this study. The digital images are taken at the meteorological measurement platform, which is situated on the roof of a university campus building and overlooking the city of Vienna. The photographs are automatically taken as part of Multi AXis Differential Optical Absorption Spectroscopy (MAX-DOAS) measurements, which are continuously performed during daytime at different azimuthal and elevation angles. Two new algorithms for estimating aerosol amounts by quantifying digital images taken on cloud-free days are introduced within the scope of the present study: (1) the ratio of the blue to red (B/R) RGB channels within a vertical pixel line and (2) the grayscale contrast fluctuations (GCF) within a horizontal pixel line. Vertical B/R and horizontal GCF are compared against ground-based aerosol optical depth (AOD) and in-situ particulate matter (PM) < 10 μm (PM10) measurements. Interestingly, the comparisons show promising results for specific azimuthal directions and during certain daytime periods, e.g. for constellations when the sun is to the side or directly behind the camera. In terms of coefficients of determination (R2), B/R correlates best with AOD during summer, while GCF shows better performance in winter. R2 values of up to 0.86 and 0.73 are found between horizontal line GCF and in-situ PM10 and between vertical line B/R and AOD, respectively. For a better interpretation of the correlations, simultaneous measurements of relative humidity (RH) are included in the analyses.

Development and parameter estimation of snowmelt models using spatial snow-cover observations from MODIS

Abstract. Given the importance of snow on different land and atmospheric processes, accurate representation of seasonal snow evolution, including distribution and melt volume, is highly imperative to any water resources development trajectories. The limitation of reliable snowmelt estimation in mountainous regions is, however, further exacerbated by data scarcity. This study attempts to develop relatively simple extended degree-day snow models driven by freely available snow-cover images. This approach offers relative simplicity and a plausible alternative to data-intensive models, as well as in situ measurements, and has a wide range of applicability, allowing for immediate verification with point measurements. The methodology employs readily available MODIS composite images to calibrate the snowmelt models on spatial snow distribution in contrast to the traditional snow-water-equivalent-based calibration. The spatial distribution of snow-cover is simulated using different extended degree-day models with parameters calibrated against individual MODIS snow-cover images for cloud-free days or a set of images representing a period within the snow season. The study was carried out in Baden-Württemberg (Germany) and in Switzerland. The simulated snow-cover data show very good agreement with MODIS snow-cover distribution, and the calibrated parameters exhibit relative stability across the time domain. Furthermore, different thresholds that demarcate snow and no-snow pixels for both observed and simulated snow cover were analyzed to evaluate these thresholds' influence on the model performance and identified for the study regions. The melt data from these calibrated snow models were used as standalone inputs to a modified Hydrologiska Byråns Vattenbalansavdelning (HBV) without the snow component in all the study catchments to assess the performance of the melt outputs in comparison to a calibrated standard HBV model. The results show an overall increase in Nash–Sutcliffe efficiency (NSE) performance and a reduction in uncertainty in terms of model performance. This can be attributed to the reduction in the number of parameters available for calibration in the modified HBV and an added reliability of the snow accumulation and melt processes inherent in the MODIS calibrated snow model output. This paper highlights that the calibration using readily available images used in this method allows for a flexible regional calibration of snow-cover distribution in mountainous areas with reasonably accurate precipitation and temperature data and globally available inputs. Likewise, the study concludes that simpler specific alterations to processes contributing to snowmelt can contribute to reliably identify the snow distribution and bring about improvements in hydrological simulations, owing to better representation of the snow processes in snow-dominated regimes.

Atmospheric aerosols properties over Indo-Gangetic Plain: A trend analysis using ground – Truth AERONET data for the year 2009–2017

The aerosols play an important role in the modification of the regional radiation budget. Long-term trend analysis of properties of atmospheric aerosols is important for policymakers and also for the study of the climatic implications. The Aerosol Robotic Network (AERONET) provides continuous ground-truth aerosol data during cloud-free days since two decades in India. Long-term atmospheric aerosol properties have been studied over four different sites of Indo-Gangetic Plain (IGP): Gandhi College rural background, Kanpur, urban and highly industrial area, Jaipur urban and desert dust influenced region, and Lahore urban and agriculture rich region respectively using AERONET data. The yearly variation of aerosol optical depth (AOD) along with angstrom exponent (AE), single scattering albedo (SSA), size distribution parameters (volume concentration and effective radius), and aerosol radiative forcing (ARF) have been analyzed for nine years from 2009 to 2017. A positive trend of AOD over Kanpur (0.0074/year), a negative trend over Lahore (−0.0054/year), Jaipur – 0.0027/year) and Gandhi college (−0.0008/year) was found. Volume concentration shows the increasing trend of fine mode particles which may be due to increased anthropogenic activities in comparison to natural aerosols over Kanpur, Lahore, Gandhi College, and Jaipur respectively. SSA values over Kanpur and Jaipur show dominancy of scattering while absorbing nature of particles over Lahore and Gandhi College were found. The trend of total atmospheric radiative forcing (ARF ATM ) over Kanpur and Jaipur locations was found to be decreased slightly but not so significantly while over Lahore and Gandhi College trend was found to increase in a significant way. The yearly trend of ARF ATM over Kanpur, Lahore, Jaipur, and Gandhi College was found to be −0.76 Wm −2 /year, 2.22 Wm −2 /year, −0.08 Wm −2 /year, and 3.09 Wm −2 /year respectively.

Polarization Raman lidar for atmospheric correction during remote sensing satellite calibration: instrument and test measurements.

A compact polarization Raman lidar has been designed and constructed for using it for atmospheric correction measurements during satellite optical sensor calibration in areas with high altitude and extremely low aerosol loading. The parameters of this lidar, such as laser wavelength, telescope diameter and interference filter bandwidth, were simulated and optimized for the best observation performance. The instrument has low weight, is small in size, and requires air cooling instead of commonly used water-cooling of the laser. Thus, the instrument is suitable for autonomous operation in remote sites. The lidar prototype was installed in Lijiang (26°43' N, 100°01' E), China, a potential observation site for calibrations of optical sensors of satellites. This observation site has been shown to be an appropriate place for remote sensing and satellite calibration activities with low aerosol loading, thin air and a comparably high proportion of cloud-free days. A field campaign carried out between November 2019 and April 2020 allowed for thoroughly testing the instruments. The results of test observations show that complete overlap between emitted laser beam and field-of-view of the receiver unit is achieved at relatively low heights above ground. The measurement accuracy is comparably high. Thus, this instrument is suitable for operating in areas with relatively clean atmospheric conditions.

Development of a three-source remote sensing model for estimation of urban evapotranspiration

Estimation of urban evapotranspiration (ET) is meaningful for evaluating the urban heat island effect and promoting urban water conservation. While multi-source satellite-based ET models for farmland and natural ecosystems have been developed, these models lack the object-oriented solutions for the urban areas. In view of this, the present study proposed a Three-source Remote sensing model for Urban areas (TRU) to simulate the urban ET, which can discriminate between urban soil evaporation, vegetation transpiration, and impervious surface evaporation. The new parameterization scheme proposed herein is characterized by the theory of complementary relationship to estimate the urban soil evaporation. Evaporation losses in the impervious areas were independently estimated in accordance with the distribution of urban land use and land cover (LULC). The developed TRU model was evaluated for 63 cloud-free test days in Tianjin, China by using 30 m Landsat Operational Land Imager (OLI)/Enhanced Thematic Mapper Plus (ETM+) images. The outcomes of this study revealed a root-mean-square-error (RMSE) of 40.2 W/m2 and a bias of 10.8 W/m2 compared with the two eddy correlation (EC) observations for the instantaneous latent heat flux (LE) simulation. Moreover, the RMSE and bias were 0.092 and −0.023, respectively in simulating the vegetation latent heat flux fraction (LEv/LE) when verified with respect to the stable water isotope measurements. Due to limited water availability, the impervious urban surface evaporation magnitude was found quite low with minimal variation on different test days. The results emphasize the importance of coupling LULC information in the urban ET modeling conceptualization. Certainly, the TRU model improved the ET simulation accuracy in urban areas and could facilitate the mapping of urban ET components at high spatial resolution.

A Remote Sensing and Machine Learning-Based Approach to Forecast the Onset of Harmful Algal Bloom

In the last few decades, harmful algal blooms (HABs, also known as “red tides”) have become one of the most detrimental natural phenomena in Florida’s coastal areas. Karenia brevis produces toxins that have harmful effects on humans, fisheries, and ecosystems. In this study, we developed and compared the efficiency of state-of-the-art machine learning models (e.g., XGBoost, Random Forest, and Support Vector Machine) in predicting the occurrence of HABs. In the proposed models the K. brevis abundance is used as the target, and 10 level-02 ocean color products extracted from daily archival MODIS satellite data are used as controlling factors. The adopted approach addresses two main shortcomings of earlier models: (1) the paucity of satellite data due to cloudy scenes and (2) the lag time between the period at which a variable reaches its highest correlation with the target and the time the bloom occurs. Eleven spatio-temporal models were generated, each from 3 consecutive day satellite datasets, with a forecasting span from 1 to 11 days. The 3-day models addressed the potential variations in lag time for some of the temporal variables. One or more of the generated 11 models could be used to predict HAB occurrences depending on availability of the cloud-free consecutive days. Findings indicate that XGBoost outperformed the other methods, and the forecasting models of 5–9 days achieved the best results. The most reliable model can forecast eight days ahead of time with balanced overall accuracy, Kappa coefficient, F-Score, and AUC of 96%, 0.93, 0.97, and 0.98 respectively. The euphotic depth, sea surface temperature, and chlorophyll-a are always among the most significant controlling factors. The proposed models could potentially be used to develop an “early warning system” for HABs in southwest Florida.

Comparison of Observations and Predictions of Daytime Planetary-Boundary-Layer Heights and Surface Meteorological Variables in the Columbia River Gorge and Basin During the Second Wind Forecast Improvement Project

The second Wind Forecast Improvement Project (WFIP2) is an 18-month field campaign in the Pacific Northwest U.S.A., whose goal is to improve the accuracy of numerical-weather-prediction forecasts in complex terrain. The WFIP2 campaign involved the deployment of a large suite of in situ and remote sensing instrumentation, including eight 915-MHz wind-profiling radars, and surface meteorological stations. The evolution and annual variability of the daytime convective planetary-boundary-layer (PBL) height is investigated using the wind-profiling radars. Three models with different horizontal grid spacing are evaluated: the Rapid Refresh, the High-Resolution Rapid Refresh, and its nested version. The results are used to assess errors in the prediction of PBL height within the experimental and control versions of the models, with the experimental versions including changes and additions to the model parametrizations developed during the field campaign, and the control version using the parametrizations present in the National Oceanic and Atmospheric Administration/National Centers for Environmental Prediction operational version of the models at the start of the project. Results show that the high-resolution models outperform the low-resolution versions, the experimental versions perform better compared with the control versions, model PBL height estimations are more accurate on cloud-free days, and model estimates of the PBL height growth rate are more accurate than model estimates of the rate of decay. Finally, using surface sensors, we assess surface meteorological variables, finding improved surface irradiance and, to a lesser extent, improved 2-m temperature in the experimental version of the model.

Evaluation of UV–visible MAX-DOAS aerosol profiling products by comparison with ceilometer, sun photometer, and in situ observations in Vienna, Austria

Abstract. Since May 2017 and August 2018, two ground-based MAX-DOAS (multi-axis differential optical absorption spectroscopy) instruments have been continuously recording daytime spectral UV–visible measurements in the northwest (University of Natural Resources and Life Sciences (BOKU) site) and south (Arsenal site), respectively, of the Vienna city center (Austria). In this study, vertical aerosol extinction (AE) profiles, aerosol optical depth (AOD), and near-surface AE are retrieved from MAX-DOAS measurements recorded on cloud-free days applying the Bremen Optimal estimation REtrieval for Aerosols and trace gaseS (BOREAS) algorithm. Measurements of atmospheric profiles of pressure and temperature obtained from routinely performed sonde ascents are used to calculate box-air-mass factors and weighting functions for different seasons. The performance of BOREAS was evaluated against co-located ceilometer, sun photometer, and in situ instrument observations covering all four seasons. The results show that the vertical AE profiles retrieved from the BOKU UV–visible MAX-DOAS observations are in very good agreement with data from the co-located ceilometer, reaching correlation coefficients (R) of 0.936–0.996 (UV) and 0.918–0.999 (visible) during the fall, winter, and spring seasons. Moreover, AE extracted using the lowest part of MAX-DOAS vertical profiles (up to 100 m above ground) is highly consistent with near-surface ceilometer AE (R&gt;0.865 and linear regression slopes of 0.815–1.21) during the fall, winter, and spring seasons. A strong correlation is also found for the BOREAS-based AODs when compared to the AERONET ones. Notably, the highest correlation coefficients (R=0.953 and R=0.939 for UV and visible, respectively) were identified for the fall season. While high correlation coefficients are generally found for the fall, winter, and spring seasons, the results are less reliable for measurements taken during summer. For the first time, the spatial variability of AOD and near-surface AE over the urban environment of Vienna is assessed by analyzing the retrieved and evaluated BOREAS aerosol profiling products in terms of different azimuth angles of the two MAX-DOAS instruments and for different seasons. We found that the relative differences of averaged AOD between different azimuth angles are 7–13 %, depending on the season. Larger relative differences of up to 32 % are found for near-surface AE in the different azimuthal directions. This study revealed the strong capability of BOREAS to retrieve AE profiles, AOD, and near-surface AE over urban environments and demonstrated its use for identifying the spatial variability of aerosols in addition to the temporal variation.

Cloud property trends in hot and ultra-hot giant gas planets (WASP-43b, WASP-103b, WASP-121b, HAT-P-7b, and WASP-18b)

Context. Ultra-hot Jupiters are the hottest exoplanets that have been discovered so far. Observations begin to provide insight into the composition of their extended atmospheres and their chemical day/night asymmetries. Both are strongly affected by cloud formation. Aims. We explore trends in cloud properties for a sample of five giant gas planets: the hot gas giant WASP-43b and the four ultra-hot Jupiters (UHJs) WASP-18b, HAT-P-7b, WASP-103b, and WASP-121b. This provides a reference frame for cloud properties for the JWST targets WASP-43b and WASP-121b. We further explore chemically inert tracers to observe geometrical asymmetries of UHJs and if the location of the inner boundary of a 3D global circulation model (3D GCM) matters for the clouds that form. Methods. A homogeneous set of 3D GCM results was used as input for a kinetic cloud formation code to evaluate the cloud opacity and gas parameters such as C/O, mean molecular weight, and degree of ionisation. We cast our results in terms of integrated quantities to enable a global comparison between the sample planets. Results. The large day/night temperature differences of UHJs cause the following chemical asymmetries: cloud-free days but cloudy nights, atomic versus molecular gases and their different mean molecular weights, deep thermal ionospheres versus low-ionised atmospheres, and undepleted versus enhanced C/O. WASP-18b, as the heaviest planet in the sample, has the lowest global C/O. Conclusions. The global climate may be considered as similar amongst UHJs, but different to that of hot gas giants. The local weather, however, is individual for each planet since the local thermodynamic conditions, and hence the local cloud and gas properties, differ. The morning and the evening terminator of UHJs will carry signatures of their strong chemical asymmetry such that ingress and egress asymmetries can be expected. An increased C/O ratio is a clear sign of cloud formation, making cloud modelling a necessity when utilising C/O (or other mineral ratios) as a tracer for planet formation. The changing geometrical extension of the atmosphere from the day to the nightside may be probed through chemically inert species such as helium. Ultra-hot Jupiters are likely to develop deep atmospheric ionospheres which may impact the atmosphere dynamics through magneto-hydrodynamic processes.

Spaceborne C-band SAR remote sensing-based flood mapping and runoff estimation for 2019 flood scenario in Rupnagar, Punjab, India.

Floods are one of the most disastrous and dangerous catastrophes faced by humanity for ages. Though mostly deemed a natural phenomenon, floods can be anthropogenic and can be equally devastating in modern times. Remote sensing with its non-evasive data availability and high temporal resolution stands unparalleled for flood mapping and modelling. Since floods in India occur mainly in monsoon months, optical remote sensing has limited applications in proper flood mapping owing to lesser number of cloud-free days. Remotely sensed microwave/synthetic aperture radar (SAR) data has penetration ability and has high temporal data availability, making it both weather independent and highly versatile for the study of floods. This study uses space-borne SAR data in C-band with VV (vertically emitted and vertically received) and VH (vertically emitted and horizontally received) polarization channels from Sentinel-1A satellite for SAR interferometry-based flood mapping and runoff modeling for Rupnagar (Punjab) floods of 2019. The flood maps were prepared using coherence-based thresholding, and digital elevation map (DEM) was prepared by correlating the unwrapped phase to elevation. The DEM was further used for Soil Conservation Service-curve number (SCS-CN)-based runoff modelling. The maximum runoff on 18 August 2019 was 350mm while the average daily rainfall was 120mm. The estimated runoff significantly correlated with the rainfall with an R2 statistics value of 0.93 for 18 August 2019. On 18 August 2019, Rupnagar saw the most devastating floods and waterlogging that submerged acres of land and displaced thousands of people.

Evaluating the performance of high-resolution satellite imagery in detecting ephemeral water bodies over West Africa

Pastoralism is a major occupation in Africa’s Sahel region, which spans from Senegal in the west to Ethiopia in the east and has a semi-arid climate. Pastoralists from the region are usually nomadic, moving regularly in search of water and pasture, often for hundreds of kilometers. Traditionally, pastoralists have relied on historical knowledge and transhumance pathways to reach the water bodies (WB). Due to climatic conditions, a vast majority of the WBs in this region are small, sporadic, and ephemeral, therefore there is a need to map and frequently monitor water availability. Remote sensing observations can be used to complement current monitoring efforts. High-resolution near-daily imagery from the recently launched PlanetScope (PS) constellation of small satellites has the potential to complement the existing medium-resolution and infrequent but well established and extensively calibrated sensors like Landsat. The aim of this study is to investigate the performance and applicability of high-resolution PS data to detect and monitor WBs in the Ferlo region of West Africa, in comparison to Landsat-8 imagery. The results indicate that for cloud-free days, PS data was comparable (r >0.88; ubRMSE <0.01) to Landsat, but due to inadequate cloud masking, the results were poor for cloudy days (r < 0.49 and ubRMSE>0.058). The PS images were able to detect nearly 95% of the WBs, whereas Landsat was able to identify only 32% at the water fraction threshold >40%. Initial results from this study show that the high spatial and temporal resolution PS datasets show promise in monitoring WBs in arid regions of West Africa.

Sentinel-1 Change Detection Analysis for Cyclone Damage Assessment in Urban Environments

For disaster emergency response, timely information is critical and satellite data is a potential source for such information. High-resolution optical satellite images are often the most informative, but these are only available on cloud-free days. For some extreme weather disasters, like cyclones, access to cloud-free images is unlikely for days both before and after the main impact. In this situation, Synthetic Aperture Radar (SAR) data is a unique first source of information, as it works irrespective of weather and sunlight conditions. This paper shows, in the context of the cyclone Idai that hit Mozambique in March 2019, that Change Detection between pairs of SAR data is a perfect match with weather data, and therefore captures impact from the severe cyclone. For emergency operations, the filtering of Change Detections by external data on the location of houses prior to an event allows assessment of the impact on houses as opposed to impact on the surrounding natural environment. The free availability of SAR data from Sentinel-1, with further automated processing of it, means that this analysis is a cost-effective and quick potential first indication of cyclone destruction.

A hybrid method for reconstructing the historical evolution of aerosol optical depth from sunshine duration measurements

Abstract. A novel method has been developed to estimate aerosol optical depth (AOD) from sunshine duration (SD) measurements under cloud-free conditions. It is a physically based method serving for the reconstruction of the historical evolution of AOD during the last century. In addition to sunshine duration data, it requires daily water vapor and ozone products as inputs taken from the ECMWF 20th century reanalysis ERA-20C, available at the global scale over the period 1900–2010. Surface synoptic cloud observations are used to identify cloud-free days. For 16 sites over Europe, the accuracy of the estimated daily AOD, and its seasonal variability, is similar to or better than those from two earlier methods when compared to AErosol RObotic NETwork measurements. In addition, it also improves the detection of the signal from massive aerosol events such as important volcanic eruptions (e.g., Arenal and Fernandina Island in 1968, El Chichón in 1982 and Pinatubo in 1992). Finally, the reconstructed AOD time series are in good agreement with the dimming/brightening phenomenon and also provide preliminary evidence of the early-brightening phenomenon.

Satellite mapping of PM&amp;lt;sub&amp;gt;2.5&amp;lt;/sub&amp;gt; episodes in the wintertime San Joaquin Valley: a “static” model using column water vapor

Abstract. The use of satellite aerosol optical thickness (AOT) from imaging spectrometers has been successful in quantifying and mapping high-PM2.5 (particulate matter with a mass &lt;2.5 µm diameter) episodes for pollution abatement and health studies. However, some regions have high PM2.5 but poor estimation success. The challenges in using AOT from imaging spectrometers to characterize PM2.5 worldwide was especially evident in the wintertime San Joaquin Valley (SJV). The SJV's attendant difficulties of high-albedo surfaces and very shallow, variable vertical mixing also occur in other significantly polluted regions around the world. We report on more accurate PM2.5 maps (where cloudiness permits) for the whole winter period in the SJV (19 November 2012–18 February 2013). Intensive measurements by including NASA aircraft were made for several weeks in that winter, the DISCOVER-AQ (Deriving Information on Surface Conditions from COlumn and VERtically Resolved Observations Relevant to Air Quality) California mission. We found success with a relatively simple method based on calibration and checking with surface monitors and a characterization of vertical mixing, and incorporating specific understanding of the region's climatology. We estimate PM2.5 to within ∼7 µg m−3 root mean square error (RMSE) and with R values of ∼0.9, based on remotely sensed multi-angle implementation of atmospheric correction (MAIAC) observations, and certain further work will improve that accuracy. Mapping is at 1 km resolution. This allows a time sequence of mapped aerosols at 1 km for cloud-free days. We describe our technique as a “static estimation.” Estimation procedures like this one, not dependent on well-mapped source strengths or on transport error, should help full source-driven simulations by deconstructing processes. They also provide a rapid method to create a long-term climatology. Essential features of the technique are (a) daily calibration of the AOT to PM2.5 using available surface monitors, and (b) characterization of mixed layer dilution using column water vapor (CWV, otherwise “precipitable water”). We noted that on multi-day timescales both water vapor and particles share near-surface sources and both fall to very low values with altitude; indeed, both are largely removed by precipitation. The existence of layers of H2O or aerosol not within the mixed layer adds complexity, but mixed-effects statistical regression captures essential proportionality of PM2.5 and the ratio variable (AOT ∕ CWV). Accuracy is much higher than previous statistical models and can be extended to the whole Aqua satellite data record. The maps and time series we show suggest a repeated pattern for large valleys like the SJV – progressive stabilization of the mixing height after frontal passages: PM2.5 is somewhat more determined by day-by-day changes in mixing than it is by the progressive accumulation of pollutants (revealed as increasing AOT).