Effect of the Ushant SST front on regional weather forecasting in the event of anticyclonic events

The representation of the Ushant front in Meteo-France numerical models is not accurate. The aim of this study is to evaluate the impact of a better representation of this front derived from SST satellite observation data on the weather forecast in Brittany.&#160;The study consisted in finding and selecting cases from 2016 to 2018 where the Ushant front was present in satellite SST analysis (high spatial and temporal resolution ) with differences in weather pattern between North and South Brittany. Then compare this to the operational Arome model (Meteo France non hydrostatic model).Situations of disagreement between the model and the observations were selected. Some weather forecast simulations with Mesonh model (very close to Arome) were performed on these cases with a better definition of the Ushant front. We present some results.&#160;&#160;

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Method of identification of the ranges of (non)acceptable factor values to reduce the risk of freight car derailment due to broken bogie solebar

Dependability ◽

10.21683/1729-2646-2019-19-3-40-46 ◽

2019 ◽

Vol 19 (3) ◽

pp. 40-46

Author(s):

I. B. Shubinsky ◽

A. M. Zamyshliaev ◽

A. N. Ignatov ◽

A. I. Kibzun ◽

E. O. Novozhilov

Keyword(s):

Quantitative Characteristic ◽

Hazard Index ◽

Mixed Integer ◽

Rolling Stock ◽

Observation Data ◽

Car Crash ◽

Impact Index ◽

Freight Car ◽

Definition Of ◽

The Impact

Aim. According to the Russian freight car crash/derailment investigation records for the period between 2013 and 2016., derailments and crashes during train operations were mostly caused by rolling stock malfunctions, while about a third of such derailments were due to bogie solebar fracture. The average number of derailed units of rolling stock is 4.16 in case of derailment due to solebar fracture against 1.73 in case of derailments due to other rolling stock malfunctions. Previously, a method was developed that allows making decisions to discard a batch of solebars. On the other hand, solebars from batches exempt from discarding can be subject to fractures over time. In this context, it appears to be of relevance to develop a method that would enable timely uncoupling of a car for its submission to depot/full repairs in order to avoid solebar fracture. For this purpose, factor models of fracture hazard estimation should be considered. Such factors may include the number of kilometers travelled from the last maintenance depot (MD), as well as the number of kilometers and days until the next scheduled full/depot repairs. The probability of solebar fracture can be used as the quantitative characteristic of the hazard of solebar fracture. However, probability estimation in the form of, for instance, the frequency of solebar fracture is only possible when observation data is available on when fracture or critical defect of solebar did not occur, yet such data is not collected. Therefore, the hazard index of solebar fracture should be developed. As it is difficult to manage the frequency of car submission to MD, the hazard index must depend only on the number of days and kilometers to repairs. Using the constructed index, the ranges of (non) acceptable factor values must be defined in order to enable decision-making regarding car uncoupling and submission to repairs, should the MD car inspector have doubts regarding the necessity of uncoupling. Methods. Methods of mathematical programming were used in this paper. Results. Conclusions. An impact index was built that characterizes the probability of freight car solebar fracture depending on the number of days and kilometers until the next scheduled repairs of such car. Based on that index, two methods of definition of ranges of (non)acceptable factor values were proposed. The first method was based on the values of the impact index. The second one was based on the identification of some parameters of ranges of (non)acceptable factor values and selection – out of all ranges – of the best ones in terms the lowest hazard of solebar fracture. Such selection was made by solving problems of mixed integer programming with quadratic constraint.

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The Met Office Weather Game: investigating how different methods for presenting probabilistic weather forecasts influence decision-making

Geoscience Communication ◽

10.5194/gc-2-101-2019 ◽

2019 ◽

Vol 2 (2) ◽

pp. 101-116 ◽

Cited By ~ 1

Author(s):

Elisabeth M. Stephens ◽

David J. Spiegelhalter ◽

Ken Mylne ◽

Mark Harrison

Keyword(s):

Decision Making ◽

Weather Forecasting ◽

Weather Forecast ◽

Online Game ◽

Weather Forecasts ◽

Probabilistic Forecasts ◽

Presentation Formats ◽

The Uk ◽

Mass Participation ◽

The Impact

Abstract. To inform the way probabilistic forecasts would be displayed on their website, the UK Met Office ran an online game as a mass participation experiment to highlight the best methods of communicating uncertainty in rainfall and temperature forecasts, and to widen public engagement in uncertainty in weather forecasting. The game used a hypothetical “ice-cream seller” scenario and a randomized structure to test decision-making ability using different methods of representing uncertainty and to enable participants to experience being “lucky” or “unlucky” when the most likely forecast scenario did not occur. Data were collected on participant age, gender, educational attainment, and previous experience of environmental modelling. The large number of participants (n>8000) that played the game has led to the collation of a unique large dataset with which to compare the impact on the decision-making ability of different weather forecast presentation formats. This analysis demonstrates that within the game the provision of information regarding forecast uncertainty greatly improved decision-making ability and did not cause confusion in situations where providing the uncertainty added no further information.

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An Improved Method Combining ANN and 1D-Var for the Retrieval of Atmospheric Temperature Profiles from FY-4A/GIIRS Hyperspectral Data

Remote Sensing ◽

10.3390/rs13030481 ◽

2021 ◽

Vol 13 (3) ◽

pp. 481

Author(s):

Pengyu Huang ◽

Qiang Guo ◽

Changpei Han ◽

Chunming Zhang ◽

Tianhang Yang ◽

...

Keyword(s):

Neural Network ◽

Satellite Observation ◽

Hyperspectral Data ◽

Radiative Transfer Model ◽

Average Error ◽

Transfer Model ◽

Observation Data ◽

Temperature Profiles ◽

Retrieval Method ◽

The Impact

In our study, a retrieval method of temperature profiles is proposed which combines an improved one-dimensional variational algorithm (1D-Var) and artificial neural network algorithm (ANN), using FY-4A/GIIRS (Geosynchronous Interferometric Infrared Sounder) infrared hyperspectral data. First, according to the characteristics of the FY-4A/GIIRS observation data using the conventional 1D-Var, we introduced channel blacklists and discarded the channels that have a large negative impact on retrieval, then used the information capacity method for channel selection and introduced a neural network to correct the satellite observation data. The improved 1D-Var effectively used the observation information of 1415 channels, reducing the impact of the error of the satellite observation and radiative transfer model, and realizing the improvement of retrieval accuracy. We subsequently used the improved 1D-Var and ANN algorithms to retrieve the temperature profiles, respectively, from the GIIRS data. The results showed that the accuracy when using ANN is better than using improved 1D-Var in situations where the pressure ranges from 800 hPa to 1000 hPa. Therefore, we combined the improved 1D-Var and ANN method to retrieve temperature profiles for different pressure levels, calculating the error by taking sounding data published by the University of Wyoming as the true values. The results show that the average error of the retrieved temperature profiles is smaller than 2 K when using our method, this method makes the accuracy of the retrieved temperature profiles superior to the accuracy of the GIIRS products from 10 hPa to 575 hPa. All in all, through the combination of the physical retrieval method and the machine learning retrieval method, this paper can certainly provide a reference for improving the accuracy of products.

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Evaluation of Virtual Reference Station Constraints for GNSS Tropospheric Tomography in Austria Region

Advances in Geosciences ◽

10.5194/adgeo-50-39-2019 ◽

2019 ◽

Vol 50 ◽

pp. 39-48 ◽

Cited By ~ 2

Author(s):

Zohreh Adavi ◽

Robert Weber

Keyword(s):

Unique Solution ◽

Weather Forecasting ◽

Weather Forecast ◽

Reference Station ◽

Virtual Reference ◽

Area Of Interest ◽

Pass Through ◽

Gnss Network ◽

The Impact

Abstract. One of the most promising methods of GNSS meteorology is GNSS Tomography. This method can be used for the determination of water vapor distribution, which contributes to the reliability of weather forecasting and early warning of severe weather. Therefore, GNSS Tomography is a valuable source of information for meteorological and weather forecast. The system of equations of this problem is mixed-determined because propagated signals do not pass through some of the model elements within the area of interest. Consequently, the normal matrix is close to singular without any unique solution. To avoid singularity and achieve a unique solution, additional sources or horizontal and/or vertical constraints are usually applied. Here, three schemes have been considered for remedying the rank deficiency of the problem. In the first scheme, minimum horizontal and vertical constraints were imposed on the system of observation equations. Then, we have defined three schemes to evaluate the impact of Virtual Reference Stations (VRS) in comparison to horizontal and vertical constraints in the sparse GNSS network. Within a network of Austrian GNSS reference stations these schemes have been analyzed and validated with available radiosonde profiles for the period DoY 245–256 in 2017. According to our results, the consistency of the estimated refractivity field with radiosonde profiles in the dense GNSS network was generally better (RMSE 2.80 ppm) than for the two other schemes in the period of interest. Moreover, in the sparse GNSS network, the average of RMSE for schemes with VRS stations and constraints equation was about 3.02 and 3.27 ppm, respectively. Hence, the obtained results illustrate that applying VRS stations in the sparse GNSS network can lead to a better solution in comparison to applying horizontal and vertical constraints.

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PENGARUH ASIMILASI MODEL CUACA WEATHER RESEARCH FORECAST (WRF) DENGAN DATA RADIASI SATELIT TERHADAP ESTIMASI CURAH HUJAN (Studi Kasus Stasiun Meteorologi Pattimura‒Ambon Tanggal 24-25 Juli 2013)

Prosiding SNFA (Seminar Nasional Fisika dan Aplikasinya) ◽

10.20961/prosidingsnfa.v3i0.28524 ◽

2019 ◽

Vol 3 ◽

pp. 118

Author(s):

Habib Burrahman ◽

Andreas Kurniawan Silitonga ◽

Ilham Haris Batubara ◽

Ahmad Fadlan

Keyword(s):

Time Series ◽

Numerical Models ◽

Wrf Model ◽

Weather Forecast ◽

Skill Score ◽

Experimental Models ◽

Observation Data ◽

Initial Condition ◽

Rainfall Forecasting ◽

Numerical Weather

Abstract: Numerical weather predictions are currently being developed to address the need for high resolution rainfall forecasting. However, numerical weather forecasts in Indonesia are still problematic in terms of the accuracy of numerical models. Several previous studies have shown that modeling accuracy is strongly influenced by errors in the initial condition data. This study examines efforts from the research and development of the Weather Forecast and Forecast (WRF) model of preliminary data using a satellite beam assimilation procedure for forecasting rainfall in the Ambon region for two different case studies in 2018. Six experimental models are carried out by assimilation of sensors AMSU-A and MHS satellites use the WRFDA 3DVar system. This research was conducted by increasing the assimilation analysis on the initial data model, analyzing the model skills in the dichotomy of rainfall predictions, rainfall criteria, spatial rainfall, and time series of rainfall accumulation compared to BMKG rainfall observation data. The results showed that the DA AMSU-A and MHS experiments correctly modified the initial condition data of the model. Meanwhile, the results of dichotomous verification revealed that the DA observation experiment had the highest skill score forecast compared to other assimilation. but more experiments are needed in the northern Sumatra area to provide more significant results.Abstrak: Prediksi cuaca numerik saat ini terus dikembangkan untuk mengatasi kebutuhan akan ramalan curah hujan resolusi tinggi. Namun, ramalan cuaca numerik di Indonesia masih bermasalah dalam hal akurasi model numerik. Beberapa penelitian sebelumnya menunjukkan bahwa akurasi pemodelan sangat dipengaruhi oleh kesalahan dalam data kondisi awal. Penelitian ini mengkaji upaya-upaya dari penelitian dan pengembangan model Prakiraan Cuaca dan Prakiraan (WRF) data awal menggunakan prosedur asimilasi pancaran satelit untuk prakiraan curah hujan di wilayah Ambon untuk dua studi kasus pada musim yang berbeda selama 2018. Enam model eksperimental dijalankan dengan asimilasi sensor satelit AMSU-A dan MHS menggunakan WRFDA sistem 3DVar. Penelitian ini dilakukan dengan analisis peningkatan asimilasi pada model data awal, analisis keterampilan model pada dikotomi prediksi curah hujan, kriteria curah hujan, curah hujan spasial, dan time series akumulasi hujan dibandingkan dengan data pengamatan curah hujan BMKG. Hasil penelitian menunjukkan bahwa eksperimen DA AMSU-A dan MHS memodifikasi data kondisi awal model dengan benar. Sementara itu, hasil verifikasi dikotomis mengungkapkan bahwa eksperimen DA observasi memiliki skor ketrampilan prakiraan tertinggi dibandingkan dengan asimilasi lainnya. namun diperlukan lagi percobaan di daerah Sumatra utara untuk memberikan hasil yang lebih signifikan.

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High-resolution smoke forecasting for the 2018 Camp Fire in California

Bulletin of the American Meteorological Society ◽

10.1175/bams-d-20-0329.1 ◽

2021 ◽

Keyword(s):

Anthropogenic Pollution ◽

Satellite Observation ◽

Test Case ◽

Observation Data ◽

Fire Event ◽

Pressure System ◽

Fire Smoke ◽

Radiative Power ◽

The Impact ◽

First Time

Abstract Smoke from the 2018 Camp Fire in Northern California blanketed a large part of the region for two weeks, creating poor air quality in the “unhealthy” range for millions of people. The NOAA Global System Laboratory’s HRRR-Smoke model was operating experimentally in real time during the Camp Fire. Here, output from the HRRR-Smoke model is compared to surface observations of PM2.5 from AQS and PurpleAir sensors as well as satellite observation data. The HRRR-Smoke model grid at 3-km resolution successfully simulated the evolution of the plume during the initial phase of the fire (8-10 November 2018). Stereoscopic satellite plume height retrievals were used to compare with model output (for the first time, to the authors’ knowledge), showing that HRRR-Smoke is able to represent the complex 3D distribution of the smoke plume over complex terrain. On 15-16 November, HRRR-Smoke was able to capture the intensification of PM2.5 pollution due to a high pressure system and subsidence that trapped smoke close to the surface; however, HRRR-Smoke later underpredicted PM2.5 levels due to likely underestimates of the fire radiative power (FRP) derived from satellite observations. The intensity of the Camp Fire smoke event and the resulting pollution during the stagnation episodes make it an excellent test case for HRRR-Smoke in predicting PM2.5 levels, which were so high from this single fire event that the usual anthropogenic pollution sources became insignificant. The HRRR-Smoke model was implemented operationally at NOAA/NCEP in December 2020, now providing essential support for smoke forecasting as the impact of US wildfires continues to increase in scope and magnitude.

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COLOR: CDOM-proxy retrieval from aeOLus ObseRvations

10.5194/egusphere-egu21-8687 ◽

2021 ◽

Author(s):

Davide Dionisi ◽

Gian Luigi Liberti ◽

Emanuele Organelli ◽

Simone Colella ◽

Marco Di Paolantonio ◽

...

Keyword(s):

Optical Properties ◽

Spectral Resolution ◽

Weather Forecasting ◽

Satellite Observation ◽

High Spectral Resolution ◽

Orthogonal Polarization ◽

Spatial And Temporal Variability ◽

Backscatter Coefficient ◽

The Impact ◽

High Spectral Resolution Lidar

The ESA Earth Explorer Wind Mission ADM-Aeolus (Atmospheric Dynamics Mission), successfully launched on 22 August 2018, has the aim to provide global observations of wind profiles, demonstrating the impact of wind profile data on operational weather forecasting and on climate research. Within the Aeolus+ Innovation program, ESA has launched an Invitation To Tender (ITT, ESA AO/1-9544/20/I/NS) to carry out studies aimed at exploring, developing and validating innovative products and applications and exploiting the novel nature of Aeolus data.Lidar technique has been extensively employed in oceanography, mainly through shipborne and aircraft lidars [1],[2]. Recently, new applications using CALIOP (Cloud-Aerosol Lidar with Orthogonal Polarization) instrument on-board CALIPSO (Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observation) demonstrated that satellite-borne lidar can give valuable information about ocean optical properties [3],[4].Although Aeolus&#8217;s mission primary objectives and subsequent instrumental and sampling characteristics are not ideal for monitoring ocean sub-surface properties, the unprecedented type of measurements from this mission are expected to contain important and original information in terms of optical properties of the sensed ocean volume. Being the first HSRL (High Spectral Resolution Lidar) launched in space, ALADIN (Atmospheric LAser Doppler Instrument) of ADM-Aeolus gives an unprecedented new opportunity to investigate the information content of the 355 nm signal backscattered by the ocean sub-surface components.Based on the above considerations, COLOR (CDOM-proxy retrieval from aeOLus ObseRvations), a selected Aeolus+ Innovation ITT project, aims to evaluate and document the feasibility of deriving an in-water AEOLUS prototype product from the analysis of the ocean sub-surface backscattered component of the 355 nm signal acquired by the ALADIN. The project focuses on the potential retrieval of the ocean optical properties at 355 nm: diffuse attenuation coefficient for downwelling irradiance, Kd [m-1], and sub-surface hemispheric particulate backscatter coefficient, bbp [m-1]. In particular, being dominated by the absorption due to CDOM (Chromophoric Dissolved Organic Matter), Kd coefficient at 355 nm, Kd(355), can be used as a proxy to describe spatial and temporal variability of this variable, which contributes to regulating the Earth&#8217;s climate. An overview of the project and some preliminary results are presented.&#160;[1]&#160; B. L. Collister, R. C. Zimmerman, C. I. Sukenik, V. J. Hill, e W. M. Balch, &#171;Remote sensing of optical characteristics and particle distributions of the upper ocean using shipboard lidar&#187;, Remote Sens. Environ., vol. 215, pagg. 85&#8211;96, set. 2018, doi: 10.1016/j.rse.2018.05.032.[2]&#160; J. H. Churnside, J. W. Hair, C. A. Hostetler, e A. J. Scarino, &#171;Ocean Backscatter Profiling Using High-Spectral-Resolution Lidar and a Perturbation Retrieval&#187;, Remote Sens., vol. 10, n. 12, Art. n. 12, dic. 2018, doi: 10.3390/rs10122003.[3]&#160; M. J. Behrenfeld et al., &#171;Global satellite-observed daily vertical migrations of ocean animals&#187;, Nature, vol. 576, n. 7786, Art. n. 7786, dic. 2019, doi: 10.1038/s41586-019-1796-9.[4]&#160; D. Dionisi, V. E. Brando, G. Volpe, S. Colella, e R. Santoleri, &#171;Seasonal distributions of ocean particulate optical properties from spaceborne lidar measurements in Mediterranean and Black sea&#187;, Remote Sens. Environ., vol. 247, pag. 111889, set. 2020, doi: 10.1016/j.rse.2020.111889.

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USING SATELLITE OBSERVATION FOR EARLY WARNING OF CONVECTIVE STORM IN TEHRAN

ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences ◽

10.5194/isprsarchives-xl-1-w5-565-2015 ◽

2015 ◽

Vol XL-1-W5 ◽

pp. 565-569

Author(s):

E. Owlad

Keyword(s):

Weather Forecasting ◽

Satellite Observation ◽

Observation Data ◽

Atmospheric Models ◽

Convective Storm ◽

Synoptic Station ◽

Convective Storms ◽

Large Application ◽

Low Levels ◽

Sudden Decrease

Severe convective storms are responsible for large amount of damage each year around the world. They form an important part of the climate system by redistributing heat, moisture, and trace gases, as well as producing large quantities of precipitation. As these extreme and rare events are in mesoscale there is many uncertainty in predicting them and we can’t rely on just models. On the other hand, remote sensing has a large application in Meteorology and near real time weather forecasting, especially in rare and extreme events like convective storms that might be difficult to predict with atmospheric models. On second of June 2014, near 12UTC a sudden and strong convective storm occurred in Tehran province that was not predicted, and caused economic and human losses. In This research we used satellite observations along with synoptic station measurements to predict and monitor this storm. Results from MODIS data show an increase in the amount of cloudiness and also aerosol optical depth and sudden decrease in cloud top temperature few hours before the storm occurs. EUMETSAT images show the governing of convection before the storm occurs. With combining the observation data that shows Lake of humidity and high temperature in low levels with satellite data that reveals instability in high levels that together caused this convective, we could track the storm and decrease the large amount of damage.

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The Met Office Weather Game: investigating how different methods for presenting probabilistic weather forecasts influence decision-making

10.5194/gc-2018-17 ◽

2018 ◽

Author(s):

Elisabeth M. Stephens ◽

David J. Spiegelhalter ◽

Ken Mylne ◽

Mark Harrison

Keyword(s):

Decision Making ◽

Weather Forecasting ◽

Weather Forecast ◽

Online Game ◽

Weather Forecasts ◽

Probabilistic Forecasts ◽

Presentation Formats ◽

The Uk ◽

Mass Participation ◽

The Impact

Abstract. To inform the way probabilistic forecasts would be displayed on their website the UK Met Office ran an online game as a mass participation experiment to highlight the best methods of communicating uncertainty in rainfall and temperature forecasts, and to widen public engagement in uncertainty in weather forecasting. The game used a hypothetical ice-cream seller scenario and a randomised structure to test decision-making ability using different methods of representing uncertainty and to enable participants to experience being lucky or unlucky when the most likely forecast scenario did not occur. Data were collected on participant age, gender, educational attainment and previous experience of environmental modelling. The large number of participants (n > 8000) that played the game has led to the collation of a unique large dataset with which to compare the impact on decision-making ability of different weather forecast presentation formats. This analysis demonstrates that within the game the provision of information regarding forecast uncertainty greatly improved decision-making ability, and did not cause confusion in situations where providing the uncertainty added no further information.

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Real time weather for autonomous driving and precise road weather forecasts based on floating car data and seamless integration of Ultra-Rapid Data Assimilation and Nowcasting

10.5194/ems2021-385 ◽

2021 ◽

Author(s):

Zoi Paschalidi ◽

Walter Acevedo ◽

Meike Hellweg ◽

Thomas Kratzsch ◽

Roland Potthast ◽

...

Keyword(s):

Data Assimilation ◽

Real Time ◽

Weather Forecasting ◽

Weather Forecast ◽

Autonomous Driving ◽

Environmental Data ◽

Observation Data ◽

Model Forecast ◽

Seamless Integration ◽

Detailed Model

The growing availability of high resolved meteorological measurements coming from automobiles puts forward the possibility of developing real time weather forecast systems, which appears to be an essential key of autonomous driving enhancement. In this frame, the Fleet Weather Maps (Flotten-Wetter-Karte - FloWKar) project, a joint work of the German Meteorological Service (DWD) and the German car manufacturer AUDI AG, aims to explore how environmental data from sensors of vehicles on Germany&#8217;s roads, respecting data protection regulations, can be used in real time to improve weather forecast, nowcasting and warnings within DWD&#8217;s products. Regarding weather forecasting, an exceptionally fast data assimilation cycle with an update rate of the order of minutes is necessary. However, this cannot be achieved using standard assimilation systems. Hence, an ultra-rapid data assimilation (URDA) method has been developed. The URDA updates only a reduced version of the state variables in an existing model forecast, using different kind of observation data available, only after a standard assimilation cycle and a full model forecast. Moreover, the quality of the meteorological data collected by moving vehicles is vital and therefore a series of quality control and bias correction algorithms has been built for the correction of the raw observations, employing among others artificial intelligence techniques. The first preliminary results of both project partners are promising: the corrected measured variables of the mass-produced vehicle-based sensors match well with the &#8216;ground truth&#8217; and real time maps are produced after the assimilation of the high resolved project data. The improved and detailed model outputs for road weather forecasting are a first necessary step towards the safety on roads especially in the winter conditions and the future autonomous driving.

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