Low Cloud Detection in Multilayer Scenes using Satellite Imagery with Machine Learning Methods

The detection of multilayer clouds in the atmosphere can be particularly challenging from passive visible and infrared imaging radiometers since cloud boundary information is limited primarily to the topmost cloud layer. Yet detection of low clouds in the atmosphere is important for a number of applications, including aviation nowcasting and general weather forecasting. In this work, we develop pixel-based machine learning-based methods of detecting low clouds, with a focus on improving detection in multilayer cloud situations and specific attention given to improving the Cloud Cover Layers (CCL) product, which assigns cloudiness in a scene into vertical bins. The Random Forest (RF) and Neural Network (NN) implementations use inputs from a variety of sources, including GOES Advanced Baseline Imager (ABI) visible radiances, infrared brightness temperatures, auxiliary information about the underlying surface, and relative humidity (which holds some utility as a cloud proxy). Training and independent validation enlists near-global, actively-sensed cloud boundaries from the radar and lidar systems onboard the CloudSat and CALIPSO satellites. We find that the RF and NN models have similar performances. The probability of detection (PoD) of low cloud increases from 0.685 to 0.815 when using the RF technique instead of the CCL methodology, while the false alarm ratio decreases. The improved PoD of low cloud is particularly notable for scenes that appear to be cirrus from an ABI perspective, increasing from 0.183 to 0.686. Various extensions of the model are discussed, including a nighttime-only algorithm and expansion to other satellite sensors.

‘Below Minima' conditions of weather over New Delhi (Palam) and their simultaneity of occurrence at New Delhi (Safdarjung), Agra, Allahabad, Lucknow and Jodhpur

An analysis of the frequency and duration of spells of poor visibility and of low loud ceiling over Palam, New Delhi, below certain specified limits has been presented using the data for the period 1949-53.Below minima condition prevailing, simultaneous over neighbouring airfield at Safdarjung are also presented. Simultaneity of weather conditions below specified limits over Agra, Allahabad, Lucknow and Jodhpur have also been studied with a view to assess their comparative suitability as alternates for Palam. It has been found that on occasions of poor visibility over Palam, Safdarjung too may be affected simultaneously on nearly 60-pre cent of the occasions. Agra is affected on 30 per cent and Allahabad, Lhcknow and Jodhpur on only less than 12 per cent of the total number of occasions over Palam. Among these, Allahabad and Lucknow are India more suitable as alternates for Palam during May and June while Jodhpur is better during the winter month .In regard to low clouds, Safdarjung is generally affected simultaneously with Palam on nearly 70 per cent of the occasions. On occasions of ‘below minima’ conditions of visibility or of low clouds. or both over Palam the suitability or aerodromes mentioned above as diversionaries stands in the following order of preference--(l) Jodhpur,(2) Lucknow, (3) Allahabad and (4) Agra.

Albedo susceptibility of Northeastern Pacific stratocumulus: the role of covarying meteorological conditions

Quantification of the radiative adjustment of marine low-clouds to aerosol perturbations, regionally and globally, remains the largest source of uncertainty in assessing current and future climate. An important step towards quantifying the role of aerosol in modifying cloud radiative properties is to quantify the susceptibility of cloud albedo and liquid water path (LWP) to perturbations in cloud droplet number concentration (Nd). We use 10 years of space-borne observations from the polar-orbiting Aqua satellite, to quantify the albedo susceptibility of marine low-clouds over the northeast (NE) Pacific stratocumulus region to Nd perturbations. Overall, we find a low-cloud brightening potential of 20.8 ± 0.96 W m−2 ln(Nd)−1, despite an overall negative LWP adjustment for non-precipitating marine stratocumulus, owing to the high occurrence (37% of the time) of thin non-precipitating clouds (LWP < 55 g m−2) that exhibit brightening. In addition, we identify two more susceptibility regimes, the entrainment-darkening regime (36% of the time), corresponding to negative LWP adjustment, and the precipitating-brightening regime (22% of the time), corresponding to precipitation suppression. The influence of large-scale meteorological conditions, obtained from the ERA5 reanalysis, on the albedo susceptibility is also examined. Over the NE Pacific, clear seasonal covariabilities among meteorological factors related to the large-scale circulation are found to play an important role in grouping favorable conditions for each susceptibility regime. Our results indicate that, for the NE Pacific stratocumulus deck, the strongest positively susceptible cloud states occur most frequently for low cloud top height (CTH), the highest lower-tropospheric stability (LTS), low sea-surface temperature (SST), and the lowest free-tropospheric relative humidity (RHft) conditions, whereas cloud states that exhibit negative LWP adjustment occur most frequently under high CTH and intermediate LTS, SST, and RHft conditions. The warm rain suppression driven cloud brightening is found to preferably occur either under unstable atmospheric conditions (low LTS) or high RHft conditions that co-occur with warm SST. Mutual information analyses reveal a dominating control of LWP, Nd and CTH (cloud state indicators) on low-cloud albedo susceptibility, rather than of the meteorological factors that drive these cloud states.

Model development in practice: A comprehensive update to the boundary layer schemes in HARMONIE-AROME cycle 40

The parameterised description of subgrid-scale processes in the clear and cloudy boundary layer has a strong impact on the performance skill in any Numerical Weather Prediction (NWP) or climate model and is still a prime source of uncertainty. Yet, improvement of this parameterised description is hard because operational models are highly optimised and contain numerous compensating errors. Therefore, improvement of a single parameterised aspect of the boundary layer often results in an overall deterioration of the model as a whole. In this paper we will describe a comprehensive integral revision of three parameterisation schemes in the HARMONIE-AROME model that together parameterise the boundary layer processes: the cloud scheme, the turbulence scheme, and the shallow cumulus convection scheme. One of the major motivations for this revision is the poor representation of low clouds in the current model cycle. The new revised parametric descriptions provide not only an improved prediction of low clouds but also of precipitation. Both improvements can be related to a stronger accumulation of moisture under the atmospheric inversion. The three improved parameterisation schemes are included in a recent update of the HARMONIE-AROME configuration, but its description and the insights in the underlying physical processes are of more general interest as the schemes are based on commonly applied frameworks. Moreover, this work offers an interesting look behind the scenes of how parameterisation development requires an integral approach and a delicate balance between physical realism and pragmatism.

Spatial distribution and interannual variability of coastal fog and low clouds cover in the hyperarid Atacama Desert and implications for past and present Tillandsia landbeckii ecosystems

The hyperarid Atacama Desert coast receives scarce moisture inputs mainly from the Pacific Ocean in the form of marine advective fog. The collected moisture supports highly specialized ecosystems, where the bromeliad Tillandsia landbeckii is the dominant species. The fog and low clouds (FLCs) on which these ecosystems depend are affected in their interannual variability and spatial distribution by global phenomena, such as ENSO. Yet, there is a lack of understanding of how ENSO influences recent FLCs spatial changes and their interconnections and how these variations can affect existing Tillandsia stands. In this study, we analyze FLCs occurrence, its trends and the influence of ENSO on the interannual variations of FLCs presence by processing GOES satellite images (1995–2017). Our results show that ENSO exerts a significant influence over FLCs interannual variability in the Atacama at ~ 20°S. Linear regression analyses reveal a relation between ENSO3.4 anomalies and FLCs with opposite seasonal effects depending on the ENSO phase. During summer (winter), the ENSO warm phase is associated with an increase (decrease) of the FLCs occurrence, whereas the opposite occurs during ENSO cool phases. In addition, the ONI Index explains up to ~ 50 and ~ 60% variance of the interannual FLCs presence in the T. landbeckii site during summer and winter, respectively. Finally, weak negative (positive) trends of FLCs presence are observed above (below) 1000 m a. s. l. These results have direct implications for understanding the present and past distribution of Tillandsia ecosystems under the extreme conditions characterizing our study area.

ANALYTICAL STUDY OF RATE VOLUME LIQUID WATER CONTENT IN LOW CLOUDS OVER IRAQ

Iraq suffers a severe declining catastrophic in water resources; due to disagreement Share water to the neighboring countries Turkey, Syria and Iran. As well as the water policy in Iraq is unclear, the management of Iraq water have no strategic plan to treat the severe decrease in water sources. In this study, adopted eight climatic stations, are available at Iraqi general meteorological organization. The low clouds data 24 observations per day had been taken, from 1975 to 2005. For this purpose, the researchers determined the distribution of the stations and water amount at the Iraq using (GIS) Technique. It has found the annual average liquid water content calculated from the Low cloud only is 2585742648 m3, and the annual average Rainfall from the low cloud is 2563537 m3. In addition, the net LWC is 2583179111 m3, so there are great opportunities to Enhancement rain from Low Clouds. To get the greatest benefit from the main low – level clouds in supplying liquid water for cloud seeding, must be given a serious attention to observing four clouds type (Sc5, Cb9, Sc8 and Cu2).

Evaluation of simulated cloud liquid water in low clouds over the Beaufort Sea in the Arctic System Reanalysis using ARISE airborne in situ observations

Arctic low clouds and the water they contain influence the evolution of the Arctic system through their effects on radiative fluxes, boundary layer mixing, stability, turbulence, humidity, and precipitation. Atmospheric models struggle to accurately simulate the occurrence and properties of Arctic low clouds, stemming from errors in both the simulated atmospheric state and the dependence of cloud properties on the atmospheric state. Knowledge of the contributions from these two factors to the model errors allows for the isolation of the process contributions to the model–observation differences. We analyze the differences between the Arctic System Reanalysis version 2 (ASR) and data taken during the September 2014 Arctic Radiation–IceBridge Sea and Ice Experiment (ARISE) airborne campaign conducted over the Beaufort Sea. The results show that ASR produces less total and liquid cloud water than observed along the flight track and is unable to simulate observed large in-cloud water content. Contributing to this bias, ASR is warmer by nearly 1.5 K and drier by 0.06 g kg−1 (relative humidity 4.3 % lower) than observed. Moreover, ASR produces cloud water over a much narrower range of thermodynamic conditions than shown in ARISE observations. Analyzing the ARISE–ASR differences by thermodynamic conditions, our results indicate that the differences are primarily attributed to disagreements in the cloud–thermodynamic relationships and secondarily (but importantly) to differences in the occurrence frequency of thermodynamic regimes. The ratio of the factors is about 2/3 to 1/3. Substantial sampling uncertainties are found within low-likelihood atmospheric regimes; sampling noise cannot be ruled out as a cause of observation–model differences, despite large differences. Thus, an important lesson from this analysis is that when comparing in situ airborne data and model output, one should not restrict the comparison to flight-track-only model output.