Meteorological Aerodrome Reports Research Articles

Spatiotemporal Variability of Convective Events in Romania Based on METAR Data

Convective weather, through its heavy showers, strong winds and hail, significantly impacts human activities, having the potential to inflict serious damage on social and environmental sectors. Limited research has been conducted on this phenomenon within Romanian territory, and currently there is no referenced climatological study primarily aimed at air traffic management users in this context. This study aims to assess the climatological aspects related to convective events based on sub-hourly observation data recorded at 17 airport weather stations throughout Romania during an 11-year period (2012–2022). The spatiotemporal distribution of convective events was analyzed based on occurrences of Cumulus Congestus (TCU) clouds, Cumulonimbus (CB) clouds, thunderstorms (TSs), heavy showers (+SHs), and hail (GR). With the data being extracted from meteorological aerodrome reports (METARs) and special meteorological aerodrome reports (SPECIs). Short-term trends were determined using Sen’s slope estimator, and statistical significance was assessed through the Mann–Kendall test. The main findings indicated that the highest occurrence of convective events is located over central and western Romania, with June emerging as the extreme month in terms of convective events, while the hourly distribution emphasizes that the highest frequency of convective events occurred in the afternoon. Trend analysis in TCU, CB, and TS show tendencies toward higher frequency of convective events while the results related to +SH and GR indicate a high variability across Romanian territory. Trend analysis disclosed more substantial changes in the TS variable. The results of this study bear potential significance for a broad spectrum of human activities and the management of natural environments.

On the Causes and Environmental Impact of Airborne Holdings at Major European Airports

This paper introduces a data-driven technique for labelling airborne holdings based on their underlying causes, specifically distinguishing between adverse weather conditions and other causes, such as airport capacity. Utilising a dataset comprised of flight trajectories arriving at 45 European airports over a nine-month period, extracted from automatic dependent surveillance-broadcast data, this paper provides valuable insights into the causes behind airborne holdings and their relative environmental impact. The proposed approach involves employing an existing neural network to identify airborne holdings. Subsequently, these holdings are cross-referenced with actual weather observations obtained from meteorological aerodrome reports. Following this, a subset of the holdings is labelled as either weather-related or attributed to other causes, based on historical air traffic flow management regulations. Finally, the cause of the majority of unlabelled holdings is determined using semi-supervised learning. The findings indicate that at least one-quarter of the 30-minute time periods with airborne holdings identified by the neural network can be attributed to weather-related factors, with reduced visibility, strong winds, and convective weather, emerging as the primary contributing events. Intriguingly, weather-related causes account for approximately 40% of the total fuel consumption associated with these procedures.

Evaluation of Probabilistic Snow Forecasts for Winter Weather Operations at Intermountain West Airports

Abstract This work set out to assess the performance of four forecast systems [the Short-Range Ensemble Forecast (SREF), High-Resolution Rapid Refresh Ensemble (HRRRE), the National Blend of Models (NBM), and the Probabilistic Snow Accumulation product (PSA) from the National Weather Service (NWS) Boulder, Colorado, Weather Forecast Office] when predicting snowfall events around the Intermountain West to advise winter weather decision-making processes at Denver International Airport. The goal was to provide airport personnel and the Boulder NWS Forecast Office with operationally relevant verification results on the timing and severity of these events so they are able to make better-informed decisions to minimize negative impacts of storms. Forecasts of snow events using various probability thresholds and a climatological snow-to-liquid ratio of 15:1 were evaluated against Meteorological Aerodrome Reports (METARs) for 24-h periods following four decision-making times spaced equally throughout the day. For the ensembles, a frequentist approach was used: the forecast probability equaled the percentage of ensemble members that predicted a snow event. The results show that the NBM had the best timing of snow events out of the products, while all the products tended to overforecast snow amount. Additionally, NBM had fewer snow events and rarely had high probabilities of snow, unlike the other forecast products.

Meteorological data source comparison—a case study in geospatial modeling of potential environmental exposure to abandoned uranium mine sites in the Navajo Nation

Meteorological (MET) data is a crucial input for environmental exposure models. While modeling exposure potential using geospatial technology is a common practice, existing studies infrequently evaluate the impact of input MET data on the level of uncertainty on output results. The objective of this study is to determine the effect of various MET data sources on the potential exposure susceptibility predictions. Three sources of wind data are compared: The North American Regional Reanalysis (NARR) database, meteorological aerodrome reports (METARs) from regional airports, and data from local MET weather stations. These data sources are used as inputs into a machine learning (ML) driven GIS Multi-Criteria Decision Analysis (GIS-MCDA) geospatial model to predict potential exposure to abandoned uranium mine sites in the Navajo Nation. Results indicate significant variations in results derived from different wind data sources. After validating the results from each source using the National Uranium Resource Evaluation (NURE) database in a geographically weighted regression (GWR), METARs data combined with the local MET weather station data showed the highest accuracy, with an average R2 of 0.74. We conclude that local direct measurement-based data (METARs and MET data) produce a more accurate prediction than the other sources evaluated in the study. This study has the potential to inform future data collection methods, leading to more accurate predictions and better-informed policy decisions surrounding environmental exposure susceptibility and risk assessment.

Quantifying the Effect of Weather on Advanced Air Mobility Operations

We quantify and analyze the potential number of flyable hours for an advanced air mobility (AAM) vehicle over the contiguous United States. We use Meteorological Aerodrome Reports (METARs) from 2019, covering 91 airports in the US. By filtering the METARs based on Federal Aviation Administration mandated flight conditions and the vehicle’s physical capabilities, our analysis shows nearly double the amount of annual acceptable flying time between the most flyable and least flyable locations in the country and identifies the largest cause of non-flyable hours as cloud cover. Our work can be used to understand the viability of AAM vehicles in a geographic location.

THE POTENTIAL OF GREENHOUSE GAS EMISSION REDUCTION USING WIND ENERGY TECHNOLOGY AT SELECTED SITES IN MALAYSIA

Wind energy is one of the five fuels that can support the reduction of greenhouse gases and climate change impact. Wind energy is one of the most promising renewable energy sources that can be exploited to succeed in 1/20 of the renewable energy mix in Malaysia. This study aimed to determine the value of GHG savings on wind energy potential based on Meteorological Aerodrome Report (METAR) data in Malaysia to establish a suitable wind farm in Kudat and Langkawi Island that can potentially generate electricity. The wind speed data were collected from the METAR report broadcasted hourly. Wind speed extrapolation was based on the selected wind turbine generator (WTG) with 950 kW and 60 kW capacity. The prospect of wind energy was analysed using the Weibull distribution to determine the parameter of shape (k) and scale (c) from the wind speed frequency distribution. Finally, the Annual Energy Production (AEP) and the GHG savings were estimated. Based on the study, the annual reduction of GHG emissions ranges from 150 to 1,300 tonnes of CO2 per year. The ten-year projection roughly estimates about 1,500 to 13,000 tonnes of CO2 per year. Implementing a low-carbon, cost-effective energy source would undoubtedly enhance Malaysia’s sustainable energy system, as proposed by the United Nations Sustainable Development Goals 7.

A climatological study of fog in Turkey

AbstractIn this study, climatological properties of fog types, their association with surrounding environment and background synoptic mechanisms triggering fog formation are investigated for Turkey. For this purpose, SYNOP (surface synoptic observations) and METAR (meteorological aerodrome report) codes of 105 stations are used for the period 2014–2019. While fog types are separated to the fog, quasi‐fog, and dense fog events according to horizontal visibility conditions, stations are classified as mountain, seaside, urban, and rural by considering the surrounding environment. Synoptic mechanisms causing the occurrence of springtime marine fog events over Black Sea are investigated using NCEP/NCAR Reanalysis. According to the main results, highest numbers of fog events are observed during winter, spring, fall, and summer months, respectively. Radiation fog frequently occurs in the mountainous regions (i.e., central and eastern part) of Turkey during winter. As a result of the rainfall during daytime, significant amount of moisture begins to saturation at night owing to rapid cooling processes and fog layer forms just above the surface. During spring, Black Sea‐effect marine fog events are frequently shown in the seaside stations of the Black Sea and northeastern Marmara regions. On the western Black Sea, winds from northeast enable rising of cold sea water to the surface (upwelling) and transfer it to the relatively warm land areas. Denser humid and cold air settling on the surface forces warm land air to rise. Additionally, warm air advection at 850 hPa (low level) creates a thick inversion layer over the region. For the eastern Black Sea, relatively cold and humid sea surface transferred to the coasts by light local northwesterly winds encounters with the mountain barrier and is trapped in the region. Furthermore, southerly winds (850 hPa) cross over the Kaçkar Mountains cause föehn effect and generate an inversion layer over the fog layer.

Evaluation of the offline-coupled GFSv15-FV3-CMAQv5.0.2 in support of the next-generation National Air Quality Forecast Capability over the contiguous United States.

As a candidate for the next-generation National Air Quality Forecast Capability (NAQFC), the meteorological forecast from the Global Forecast System with the new Finite Volume Cube-Sphere dynamical core (GFS–FV3) will be applied to drive the chemical evolution of gases and particles described by the Community Multiscale Air Quality modeling system. CMAQv5.0.2, a historical version of CMAQ, has been coupled with the North American Mesoscale Forecast System (NAM) model in the current operational NAQFC. An experimental version of the NAQFC based on the offline-coupled GFS–FV3 version 15 with CMAQv5.0.2 modeling system (GFSv15–CMAQv5.0.2) has been developed by the National Oceanic and Atmospheric Administration (NOAA) to provide real-time air quality forecasts over the contiguous United States (CONUS) since 2018. In this work, comprehensive region-specific, time-specific, and categorical evaluations are conducted for meteorological and chemical forecasts from the offline-coupled GFSv15–CMAQv5.0.2 for the year 2019. The forecast system shows good overall performance in forecasting meteorological variables with the annual mean biases of −0.2 °C for temperature at 2 m, 0.4% for relative humidity at 2 m, and 0.4 m s−1 for wind speed at 10 m compared to the METeorological Aerodrome Reports (METAR) dataset. Larger biases occur in seasonal and monthly mean forecasts, particularly in spring. Although the monthly accumulated precipitation forecasts show generally consistent spatial distributions with those from the remote-sensing and ensemble datasets, moderate-to-large biases exist in hourly precipitation forecasts compared to the Clean Air Status and Trends Network (CASTNET) and METAR. While the forecast system performs well in forecasting ozone (O3) throughout the year and fine particles with a diameter of 2.5 μm or less (PM2.5) for warm months (May–September), it significantly overpredicts annual mean concentrations of PM2.5. This is due mainly to the high predicted concentrations of fine fugitive and coarse-mode particle components. Underpredictions in the southeastern US and California during summer are attributed to missing sources and mechanisms of secondary organic aerosol formation from biogenic volatile organic compounds (VOCs) and semivolatile or intermediate-volatility organic compounds. This work demonstrates the ability of FV3-based GFS in driving the air quality forecasting. It identifies possible underlying causes for systematic region- and time-specific model biases, which will provide a scientific basis for further development of the next-generation NAQFC.

Flight time prediction for fuel loading decisions with a deep learning approach

Under increasing economic and environmental pressure, airlines are constantly seeking new technologies and optimizing flight operations to reduce fuel consumption. However, the current practice on fuel loading, which has a significant impact on aircraft weight and fuel consumption, has yet to be thoroughly addressed by existing studies. Excess fuel is loaded by dispatchers and (or) pilots to handle fuel consumption uncertainties, primarily caused by flight time uncertainties, which cannot be predicted by current Flight Planning Systems (FPS). In this paper, we develop a novel spatial weighted recurrent neural network model to provide better flight time predictions by capturing air traffic information at a national scale based on multiple data sources, including Automatic Dependent Surveillance - Broadcast (ADS-B), Meteorological Aerodrome Reports (METAR), and airline records. In this model, a spatial weighted layer is designed to extract spatial dependences among network delay states (i.e. average flight delay at each airport and average flight delay of each Origin-Destination (OD) pair for a specific time interval). Then, a new training procedure associated with the spatial weighted layer is introduced to extract OD-specific spatial weights and then integrate into one model for a nationwide air traffic network. Long short-term memory (LSTM) networks are used after the spatial weighted layer to extract the temporal behavior patterns of network delay states. Finally, features from delays, weather, and flight schedules are fed into a fully connected neural network to predict the flight time of a particular flight. The proposed model was evaluated using one year of historical data from an airline’s real operations. Results show that our model can provide a more accurate flight time predictions than baseline methods, especially for flights with extreme delays. We also show that, with the improved flight time prediction, fuel loading can be optimized and resulting reduced fuel consumption by 0.016%–1.915% without increasing the fuel depletion risk.

Blizzard Conditions in the Canadian Arctic: Observations and Automated Products for Forecasting

AbstractBlizzard conditions occur regularly in the Canadian Arctic, with high impact on travel and life there. These extreme conditions are challenging to forecast for this vast domain because the observation network is sparse and remote sensing coverage is limited. To establish occurrence statistics we analyzed METeorological Aerodrome Reports (METARs) from Canadian Arctic stations between October and May 2014-2018. Blizzard conditions occur most frequently in open tundra east and north of the boreal forest boundary, with highest frequency found on the northwest side of Hudson Bay and over flat terrain in central Baffin Island. Except in sheltered locations, the reported cause of reduced visibility is blowing snow without precipitating snow in about one-half to two-thirds of METARs made by a human observer, even higher at some stations.We produce three products that forecast blizzard conditions from post-processed NWP model output. The blizzard potential (BP), generated from expert’s rules, is intended for warning well in advance of areas where blizzard conditions may develop. A second product (BH) stems from regression equations for the probability of visibility ≤ 1 km in blowing snow and/or concurrent snow derived by Baggaley and Hanesiak (2005). A third product (RF), generated with the Random Forest ensemble classification algorithm, makes a consensus YES/NO forecast for blizzard conditions. We describe the products, provide verification, and show forecasts for a significant blizzard event. Receiver Operator Characteristic curves and critical success index scores show RF forecasts have greater accuracy than BP and BH forecasts at all lead times.

Exploring the Potential of Statistical Modeling to Retrieve the Cloud Base Height from Geostationary Satellites: Applications to the ABI Sensor on Board of the GOES-R Satellite Series.

Although cloud base height is a relevant variable for many applications, including aviation, it is not routinely monitored by current geostationary satellites. This is probably a consequence of the difficulty of providing reliable estimations of the cloud base height from visible and infrared radiances from current imagers. We hypothesize that existing algorithms suffer from the accumulation of errors from upstream retrievals necessary to estimate the cloud base height, and that this hampers higher predictability in the retrievals to be achieved. To test this hypothesis, we trained a statistical model based on the random forest algorithm to retrieve the cloud base height, using as predictors the radiances from Geostationary Operational Environmental Satellites (GOES-16) and variables from a numerical weather prediction model. The predictand data consisted of cloud base height observations recorded at meteorological aerodrome report (METAR) stations over an extended region covering the contiguous USA. Our results indicate the potential of the proposed methodology. In particular, the performance of the cloud base height retrievals appears to be superior to the state-of-the-science algorithms, which suffer from the accumulation of errors from upstream retrievals. We also find a direct relationship between the errors and the mean cloud base height predicted over the region, which allowed us to obtain estimations of both the cloud base height and its error.

Fog Forecast Using WRF Model Output for Solar Energy Applications

The occurrence of fog often causes errors in the prediction of the incident solar radiation and the power produced by photovoltaic cells. An accurate fog forecast would benefit solar energy producers and grid operators, who could take coordinated actions to reduce the impact of discontinuity, the main drawback of renewable energy sources. Considering that information on discontinuity is crucial to optimize power production estimation and plant management efficiency, in this work, a fog forecast method based on the output of the Weather Research and Forecasting (WRF) numerical model is presented. The areal extension and temporal duration of a fog event are not easy to predict. In fact, there are many physical processes and boundary conditions that cause fog development, such as the synoptic situation, air stability, wind speed, season, aerosol load, orographic influence, humidity and temperature. These make fog formation a complex and rather localized event. Thus, the results of a fog forecast method based on the output variables of the high spatial resolution WRF model strongly depend on the specific site under investigation. In this work, the thresholds are site-specifically designed so that the implemented method can be generalized to other sites after a preliminary meteorological and climatological study. The proposed method is able to predict fog in the 6–30 h interval after the model run start time; it has been evaluated against METeorological Aerodrome Report data relative to seven selected sites, obtaining an average accuracy of 0.96, probability of detection of 0.83, probability of false detection equal to 0.03 and probability of false alarm of 0.18. The output of the proposed fog forecast method can activate (or not) a specific fog postprocessing layer designed to correct the global horizontal irradiance forecasted by the WRF model in order to optimize the forecast of the irradiance reaching the photovoltaic panels surface.

Monitoring the impacts of weather radar data quality control for quantitative application at the continental scale

AbstractAs part of a suite of quality control methods applied to Canadian and American weather radar data before their assimilation into a numerical weather prediction model, the combination of thresholded depolarization ratio and a speckle filter was applied to American data with the purpose of identifying and removing non‐precipitation echoes. This polarimetric quality control replaces a set of image‐analysis‐based methods used in a previous study and based on reflectivity information only. The old and new quality‐controlled results were objectively assessed using meteorological aerodrome report (METAR)‐based precipitation occurrence observations and a set of five common contingency table skill scores with all available Next Generation Weather Radar (NEXRAD) Level II data from the contiguous United States for August 2016. The new quality control yields consistently improved skill scores, indicating higher quality radar data for downstream application. The process whereby the radar data are quality controlled and assessed comprises a framework with the ability to monitor the impacts of quality control to radar data quality over time. In turn, this allows for the introduction of changes to data acquisition and processing with the ability to monitor the impacts on data quality: a scientific evidence‐based quality assurance process as part of change management.

Assessing Bias Correction Methods in Support of Operational Weather Forecast in Arid Environment

In this study, the Weather Research and Forecasting (WRF) model is employed for operational forecasting over the United Arab Emirates (UAE). The goal of this study is to assess two bias correction methods, namely the multiplicative Ratio Correction (RC) and Kalman Filter (KF), in support of operational mesoscale forecasts in the UAE. These techniques are applied to the 2-m temperature with the corrected temperature subsequently used to update the Relative Humidity (RH) predictions. The simulation covers the 2-year period 1st January 2017 to 31st December 2018. To evaluate the WRF performance, Meteorological Aerodrome Reports (METARs) observations at five airport stations are used. It is concluded that when any of the bias correction techniques are applied, there is a significant reduction of the bias and Root-Mean-Square-Error (RMSE). This is particularly true in the summer season and during nighttime and early morning hours, when WRF has a systematic cold bias of up to 2 °C. In addition, the bias distribution is more symmetric with a reduced spread, skewness and kurtosis values. The RC technique is found to give the best scores, with the observed and modelled temperatures generally within 0.25 °C for the first two forecast days. In addition, it successfully removes the model tendency of underperforming in the warm season. A similar improvement in the skill scores is seen in the RH forecasts albeit with smaller magnitudes. The KF and RC techniques used here have been employed successfully in operational forecasts with the potential to expand them to other model variables.

Effects of Model Horizontal Grid Resolution on Short- and Medium-Term Daily Temperature Forecasts for Energy Consumption Application in European Cities

A short-term forecast of energy consumption is affected by different factors related to the demand in residential, commercial, thermoelectric, and industrial sectors. This demand can be strongly constrained by weather variables, especially temperatures, whose forecast may be very useful to predict the balances between supply and demand, minimizing the risk of price volatility. Energy companies use the relationship between meteorological forecast output and energy request to provide an effective scheduling of national gas and power grids and reduce operational costs in critical periods. This work reports a comparison analysis for short- and medium-term daily temperature forecasts during the period 2013-2014 by using the weather model e-kmf™ (eni-kassandra meteo forecast), currently adopted in gas and power applications where meteorological output has a key role. This weather forecast system uses different models and initial data to develop probabilistic predictions from a perspective of eleven days ahead. In particular, a set of model runs with horizontal grid spacing of 5.5, 8, 13, and 18 km with the same domain size are undertaken to assess the sensitivity of temperature to horizontal resolutions. A nonlinear Kalman filter has been also applied to postprocess forecasted data in eight European cities (Milano, Roma, Torino, Napoli, Munich, Paris, Brussels, and London). Filtered forecasts over these cities have been compared to local observations taken from SYNOP (surface synoptic observations) and METAR (meteorological Aerodrome Report) stations. Skill scores of performance have been used to generally assess the forecast reliability up to day +11. In order to understand the sensitivity to the horizontal resolution, investigations have been carried out even during four specific periods of two weeks with stable and unstable weather conditions.

Fog Detection Based on Meteosat Second Generation-Spinning Enhanced Visible and InfraRed Imager High Resolution Visible Channel

In this study, the Meteosat Second Generation (MSG)—Spinning Enhanced Visible and Infrared Imager (SEVIRI) High Resolution Visible channel (HRV) is used in synergy with the narrow band MSG-SEVIRI channels for daytime fog detection. A new algorithm, named MSG-SEVIRI SatFog, has been designed and implemented. MSG-SEVIRI SatFog provides the indication of the presence of fog in near real time and at the high spatial resolution of the HRV channel. The HRV resolution is useful for detecting small scale daytime fog that would be missed in the MSG-SEVIRI low spatial resolution channels. By combining textural, physical and tonal tests, a distinction between fog and low stratus is performed for pixels identified as low/middle clouds or clear by the Classification-MAsk Coupling of Statistical and Physical Methods (C-MACSP) cloud detection algorithm. Suitable thresholds have been determined using a specific dataset covering different geographical areas, seasons and time of the day. MSG-SEVIRI SatFog is evaluated against METeorological Aerodrome Reports (METAR) data observations. Evaluation results in an accuracy of 69.9%, a probability of detection of 68.7%, a false alarm ratio of 31.3% and a probability of false detection of 30.0%.

Paracas dust storms: Sources, trajectories and associated meteorological conditions

Dust storms that develop along the Pisco-Ica desert in Southern Peru, locally known as “Paracas” winds have ecological, health and economic repercussions. Here we identify dust sources through MODIS (Moderate Resolution Imaging Spectroradiometer) imagery and analyze HYSPLIT (Hybrid Single Particles Lagrangian Integrated Trajectory) model trajectories and dispersion patterns, along with concomitant synoptic-scale meteorological conditions from National Centers for Environmental Prediction/National Center for Atmospheric Research reanalysis (NCEP/NCAR). Additionally, surface pressure data from the hourly METeorological Aerodrome Report (METAR) at Arica (18.5°S, 70.3°W) and Pisco (13.7°S, 76.2°W) were used to calculate Alongshore (sea-level) Pressure Gradient (APG) anomalies during Paracas dust storms, their duration and associated wind-speeds and wind directions. This study provides a review on the occurrence and strength of the Paracas dust storms as reported in the Pisco airfield for five-year period and their correspondence with MODIS true-color imagery in terms of dust-emission source areas. Our results show that most of the particle fluxes moving into the Ica-Pisco desert area during Paracas wind events originate over the coastal zone, where strong winds forced by steep APGs develop as the axis of a deep mid-troposphere trough sets in along north-central Chile. Direct relationships between Paracas wind intensity, number of active dust-emission sources and APGs are also documented, although the scarcity of simultaneous METAR/MODIS data for clearly observed MODIS dust plumes prevents any significant statistical inference. Synoptic-scale meteorological composites from NCEP/NCAR reanalysis data show that Paracas wind events (steep APGs) are mostly associated with the strengthening of anticyclonic conditions in northern Chile, that can be attributed to cold air advection associated with the incoming trough. Compared to the MODIS images, HYSPLIT outputs were able to spatially reproduce trajectories and dust dispersion plumes during the Paracas wind storms. HYSPLIT trajectories revealed that part of the wind-eroded lithological material can be transported downwind several kilometers along the Peruvian coast and also deposited over the nearby coastal ocean, giving support to the presence of an aeolian signal in continental shelf sediments, of great importance for paleoenvironmental studies.

Cloud base height from sky imager and cloud speed sensor

Cloud base height (CBH) is a critical input to short-term solar forecasting algorithms, yet CBH measurements are difficult to obtain. Existing methods to detect CBH include radiosondes, ceilometers, and the stereographic method. However, these methods are deficient for intra-hour forecasting due to high costs or low temporal resolution. While satellite images could overcome these limitations, only the cloud top height can be determined from the thermal IR channel. We describe the integration of a cloud shadow speed sensor (CSS) with angular cloud speed from a sky imager to determine CBH. Furthermore, an improved methodology to determine cloud motion vectors from the CSS is presented, which offers lower noise and greater accuracy and stability than existing methods. Two months at the UC San Diego campus were used for validation against measurements from meteorological aerodrome reports (METAR) and an on-site ceilometer. Typical daily root mean square differences (RMSD) are 126m which corresponds to 16.9% of the observed CBH. Normalized RMSD remains below 30% for all days. The daily bias is usually less than 80m which suggests that the method is robust and that most of the RMSD is driven by short-term random fluctuations in CBH. Unlike sky image stereography the present method can be applied to measurements at a single site making it widely applicable.

Field trial of an automated ground‐based infrared cloud classification system

ABSTRACTAutomated classification of cloud types using a ground‐based infrared (IR) imager can provide invaluable high‐resolution and localized information for air traffic controllers. Observations can be made consistently, continuously in real time and accurately during both day and night operation. Details of a field trial of an automated, ground‐based IR cloud classification system are presented. The system was designed at Campbell Scientific Ltd. in collaboration with Loughborough University, UK. The main objective of the trial was to assess the performance of an automated IR camera system with a lightning detector in classifying several types of clouds, specifically cumulonimbus and towering cumulus, during continuous day and night operation. Results from the classification system were compared with those obtained from Meteorological Aerodrome Reports (METAR) and with data generated by the UK Meteorological Office from their radar‐ and sferics‐automated cloud reports system. In comparisons with METAR data, a probability of detection of up to 82% was achieved, together with a minimum probability of false detection of 18%.

Detecting ground fog from space – a microphysics-based approach

The detection of ground fog from satellite data is of interest in operational nowcasting applications, as well as in studies of the climate system. A discrimination between fog at the ground and other low-stratus situations from satellite data requires information on cloud vertical geometry to establish whether the cloud touches the ground. This article introduces a technique that allows for the discrimination between low stratus and (ground) fog on the basis of geostationary satellite imagery. The cloud-base height is derived using a subadiabatic model of cloud microphysics. In this model, the cloud base is varied until model liquid–water path matches that retrieved from satellite data. The performance of this technique is shown to be good in a comparison with METeorological Aerodrome Report data comprising 1030 satellite scenes. With a hit rate of 81% and a threat score of 0.62, the skill is satisfactory.