scholarly journals Deformation and failure of the ice bridge on the Wilkins Ice Shelf, Antarctica

2010 ◽  
Vol 51 (55) ◽  
pp. 49-55 ◽  
Author(s):  
A. Humbert ◽  
D. Gross ◽  
R. Müller ◽  
M. Braun ◽  
R.S.W. van de Wal ◽  
...  
Keyword(s):  
Prediction Models ◽  
Meteorological Data ◽  
Weather Prediction ◽  
Compressive Strain ◽  
Ice Shelf ◽  
Deformation And Failure ◽  
Position Data ◽  
Released Energy ◽  
Bridge Bent ◽  
Narrow Bridge

AbstractA narrow bridge of floating ice that connected the Wilkins Ice Shelf, Antarctica, to two confining islands eventually collapsed in early April 2009. In the month preceding the collapse, we observed deformation of the ice bridge by means of satellite imagery and from an in situ GPS station. TerraSAR-X images (acquired in stripmap mode) were used to compile a time series. The ice bridge bent most strongly in its narrowest part (westerly), while the northern end (near Charcot Island) shifted in a northeasterly direction. In the south, the ice bridge experienced compressive strain parallel to its long axis. GPS position data were acquired a little south of the narrowest part of the ice bridge from 19 January 2009. Analysis of these data showed both cyclic and monotonic components of motion. Meteorological data and re-analysis of the output of weather-prediction models indicated that easterly winds were responsible for the cyclic motion component. In particular, wind stress on the rough ice melange that occupied the area to the east exerted significant pressure on the ice bridge. The collapse of the ice bridge began with crack formation in the southern section parallel to the long axis of the ice bridge and led to shattering of the southern part. Ultimately, the narrowest part, only 900 m wide, ruptured. The formation of many small icebergs released energy of >125 ×106 J.

scholarly journals Using Ensemble of Neural Networks to Learn Stochastic Convection Parameterizations for Climate and Numerical Weather Prediction Models from Data Simulated by a Cloud Resolving Model

2013 ◽  
Vol 2013 ◽  
pp. 1-13 ◽  
Author(s):  
Vladimir M. Krasnopolsky ◽  
Michael S. Fox-Rabinovitz ◽  
Alexei A. Belochitski
Keyword(s):  
Numerical Weather Prediction ◽  
Prediction Models ◽  
Meteorological Data ◽  
Weather Prediction ◽  
Simulated Data ◽  
Boreal Winter ◽  
Ensemble Technique ◽  
Numerical Weather ◽  
Cloud Resolving Model ◽  
Convection Parameterization

A novel approach based on the neural network (NN) ensemble technique is formulated and used for development of a NN stochastic convection parameterization for climate and numerical weather prediction (NWP) models. This fast parameterization is built based on learning from data simulated by a cloud-resolving model (CRM) initialized with and forced by the observed meteorological data available for 4-month boreal winter from November 1992 to February 1993. CRM-simulated data were averaged and processed to implicitly define a stochastic convection parameterization. This parameterization is learned from the data using an ensemble of NNs. The NN ensemble members are trained and tested. The inherent uncertainty of the stochastic convection parameterization derived following this approach is estimated. The newly developed NN convection parameterization has been tested in National Center of Atmospheric Research (NCAR) Community Atmospheric Model (CAM). It produced reasonable and promising decadal climate simulations for a large tropical Pacific region. The extent of the adaptive ability of the developed NN parameterization to the changes in the model environment is briefly discussed. This paper is devoted to a proof of concept and discusses methodology, initial results, and the major challenges of using the NN technique for developing convection parameterizations for climate and NWP models.

scholarly journals MATISSE: an ArcGIS tool for monitoring and nowcasting meteorological hazards

2015 ◽  
Vol 12 (1) ◽  
pp. 163-169 ◽  
Author(s):  
V. Rillo ◽  
A. L. Zollo ◽  
P. Mercogliano
Keyword(s):  
Prediction Models ◽  
Meteorological Data ◽  
Weather Prediction ◽  
Weather Conditions ◽  
Meteorological Conditions ◽  
Economic Losses ◽  
Statistical Characterization ◽  
Adverse Weather ◽  
Aviation Hazards ◽  
Meteorological Hazards

Abstract. Adverse meteorological conditions are one of the major causes of accidents in aviation, resulting in substantial human and economic losses. For this reason it is crucial to monitor and early forecast high impact weather events. In this context, CIRA (Italian Aerospace Research Center) has implemented MATISSE (Meteorological AviaTIon Supporting SystEm), an ArcGIS Desktop Plug-in able to detect and forecast meteorological aviation hazards over European airports, using different sources of meteorological data (synoptic information, satellite data, numerical weather prediction models data). MATISSE presents a graphical interface allowing the user to select and visualize such meteorological conditions over an area or an airport of interest. The system also implements different tools for nowcasting of meteorological hazards and for the statistical characterization of typical adverse weather conditions for the airport selected.

scholarly journals Diurnal Climatology of the Boundary Layer in Southern California Using AMDAR Temperature and Wind Profiles

2016 ◽  
Vol 55 (5) ◽  
pp. 1123-1137 ◽  
Author(s):  
David A. Rahn ◽  
Christopher J. Mitchell
Keyword(s):  
Boundary Layer ◽  
Los Angeles ◽  
San Diego ◽  
Southern California ◽  
Prediction Models ◽  
Meteorological Data ◽  
Weather Prediction ◽  
Sea Breeze ◽  
Lower Atmosphere ◽  
Boundary Layer Height

AbstractObservations from commercial aircraft [e.g., the Aircraft Meteorological Data Relay (AMDAR) automated weather reports] have been increasing dramatically. Two main applications of the aircraft data are use in short-term forecasts and assimilation into numerical weather prediction models. Now that more than 10 years of measurements exist, using this dataset to construct a description of the long-term climatological behavior (a “climatology”) of the lower atmosphere is explored with two main objectives. The first objective is to examine strengths and weaknesses of using the dataset to construct a climatology of the lower atmosphere. Unlike the traditional twice-daily radiosonde launches, the high frequency of observations at major airports allows for an unprecedented set of diurnal information at many locations globally. The second objective is to obtain a climatology of the lower atmosphere of Southern California, specifically at Los Angeles, San Diego, and Ontario, during the spring and summer when the boundary layer is well defined and easily detected. The June 2001–14 climatology reveals that the deepening of the boundary layer overnight is consistent with a cloud-topped boundary layer. Whereas the average boundary layer height decreases right after sunrise at San Diego, at Los Angeles the deeper boundary layer persists about 4 h after sunrise and then decreases rapidly over 2 h as the onshore sea breeze strengthens. Morning intrusions of the marine air inland are easily detected at Ontario in some months but are practically nonexistent during July and August.

scholarly journals Ensemble flood forecasting considering dominant runoff processes: I. Setup and application to nested basins (Emme, Switzerland)

2018 ◽  
Author(s):  
Manuel Antonetti ◽  
Christoph Horat ◽  
Ioannis V. Sideris ◽  
Massimiliano Zappa
Keyword(s):  
Predictive Power ◽  
Hydrological Model ◽  
Prediction Models ◽  
Expert Knowledge ◽  
Meteorological Data ◽  
Weather Prediction ◽  
Medium Size ◽  
Hydrological Models ◽  
Runoff Generation ◽  
Forecast Performance

Abstract. Flash floods (FFs) evolve rapidly during and after heavy precipitation events and represent a risk for society. To predict the timing and magnitude of a peak runoff, it is common to couple meteorological and hydrological models in a forecasting chain. However, hydrological models rely on strong simplifying assumptions and hence need to be calibrated. This makes their application difficult in catchments where no direct observation of runoff is available. To address this gap, a FF forecasting chain is presented based on: (i) a nowcasting product which combines radar and rain gauge rainfall data (CombiPrecip), (ii) meteorological data from state-of-the-art numerical weather prediction models (COSMO-1, COSMO-E), (iii) operationally available soil moisture estimations from the PREVAH hydrological model, and (iv) a process-based runoff generation module with no need for calibration (RGM-PRO). This last component uses information on the spatial distribution of dominant runoff processes from the so-called maps of runoff types (RTs), which can be derived with different mapping approaches with increasing involvement of expert knowledge. RGM-PRO is then parametrised a priori based on the results of sprinkling experiments. This prediction chain has been evaluated using data from April to September 2016 in the Emme catchment, a medium-size FF prone basin in the Swiss Prealps. Two novel forecasting chains were set up with two different maps of RTs, which allowed sensitivity of the forecast performance on the mapping approaches to be analysed. Furthermore, special emphasis was placed on the predictive power of the new forecasting chains in nested subcatchments when compared with a prediction chain including a conventional hydrological model relying on calibration. Results showed a low sensitivity of the predictive power on the amount of expert knowledge included for the mapping approach. The forecasting chain including a map of RTs with high involvement of expert knowledge did not guarantee more skill. In the larger basins of the Emme region, process-based forecasting chains revealed comparable skill as a prediction system including a conventional hydrological model. In the small nested subcatchments, the process-based forecasting chains outperformed the conventional system, however, no forecasting chain showed satisfying skill. The outcomes of this study show that operational FF predictions in ungauged basins can benefit from the use of information on runoff processes, as no long-term runoff measurements are needed for calibration.

scholarly journals Overview of the European project FUMAPEX

2005 ◽  
Vol 5 (6) ◽  
pp. 11679-11702 ◽  
Author(s):  
A. Baklanov
Keyword(s):  
Air Pollution ◽  
Prediction Models ◽  
Meteorological Data ◽  
Weather Prediction ◽  
Urban Air Pollution ◽  
Population Exposure ◽  
Urban Air ◽  
Exposure Models ◽  
Numerical Weather Prediction Models

Abstract. The quality of the urban air pollution forecast critically depends on the mapping of emissions, the urban air pollution models, and the meteorological data. The quality of the meteorological data should be largely enhanced by using downscaled data from advanced numerical weather prediction models. These different topics, as well as the application of population exposure models, have traditionally been treated in distinct scientific communities whose expertise needs to be combined to enhance the possibilities of forecasting air pollution episodes in European cities. For this purpose the EU project ''Integrated Systems for Forecasting Urban Meteorology, Air Pollution and Population Exposure'' (FUMAPEX) (http://fumapex.dmi.dk), involving 22 organizations from 10 European countries, was initiated. The main objectives of the project are the improvement of meteorological forecasts for urban areas, the connection of numerical weather prediction models to urban air pollution and population exposure models, the building of improved Urban Air Quality Information and Forecasting Systems, and their application in cities in various European climates. This paper overviews the project items and first two-years results, it is an introduction to the whole ACP issue.

Evaluation of Accuracy of Chinese AMDAR Data for 2015

2018 ◽  
Vol 35 (5) ◽  
pp. 943-951 ◽  
Author(s):  
Jinfeng Ding ◽  
Xiaoyong Zhuge ◽  
Xin Li ◽  
Zipeng Yuan ◽  
Yuan Wang
Keyword(s):  
Wind Speed ◽  
Wind Direction ◽  
Comparative Studies ◽  
Prediction Models ◽  
Meteorological Data ◽  
Weather Prediction ◽  
Mean Square ◽  
High Confidence ◽  
Temporal Window ◽  
Numerical Weather Prediction Models

AbstractTwo comparative studies have been performed to evaluate the accuracy of Chinese Aircraft Meteorological Data Relay (AMDAR) weather reports. The comparison between AMDAR reports and radiosonde observations shows that the root-mean-square differences (RMSDs) in temperature, wind speed, and wind direction are 1.06°C, 1.95 m s−1, and 22°, respectively, within a spatial range of ≤20 km and a temporal window of ≤15 min. The comparison between AMDAR reports collected by different aircraft reveals that observation uncertainties in temperature, wind speed, and wind direction are 0.59°C, 0.90 m s−1, and 12°, respectively. The spatial and temporal representativeness as well as the environmental factors that may affect the evaluation results are also discussed in detail in the two comparative studies. The results of the present study provide valuable information on and high confidence in the application of Chinese AMDAR in numerical weather prediction models.

scholarly journals Modelling floods in the Ammer catchment: limitations and challenges with a coupled meteo-hydrological model approach

2003 ◽  
Vol 7 (6) ◽  
pp. 833-847 ◽  
Author(s):  
R. Ludwig ◽  
S. Taschner ◽  
W. Mauser
Keyword(s):  
Hydrological Model ◽  
Prediction Models ◽  
Meteorological Data ◽  
Weather Prediction ◽  
Input Parameter ◽  
Rain Gauge ◽  
Complex Nature ◽  
High Temporal Resolution ◽  
Flood Modelling ◽  
Spatial Coverage

Abstract. Numerous applications of hydrological models have shown their capability to simulate hydrological processes with a reasonable degree of certainty. For flood modelling, the quality of precipitation data — the key input parameter — is very important but often remains questionable. This paper presents a critical review of experience in the EU-funded RAPHAEL project. Different meteorological data sources were evaluated to assess their applicability for flood modelling and forecasting in the Bavarian pre-alpine catchment of the Ammer river (709 km2), for which the hydrological aspects of runoff production are described as well as the complex nature of floods. Apart from conventional rain gauge data, forecasts from several Numerical Weather Prediction Models (NWP) as well as rain radar data are examined, scaled and applied within the framework of a GIS-structured and physically based hydrological model. Multi-scenario results are compared and analysed. The synergetic approach leads to promising results under certain meteorological conditions but emphasises various drawbacks. At present, NWPs are the only source of rainfall forecasts (up to 96 hours) with large spatial coverage and high temporal resolution. On the other hand, the coarse spatial resolution of NWP grids cannot yet address, adequately, the heterogeneous structures of orographic rainfields in complex convective situations; hence, a major downscaling problem for mountain catchment applications is introduced. As shown for two selected Ammer flood events, a high variability in prediction accuracy has still to be accepted at present. Sensitivity analysis of both meteo-data input and hydrological model performance in terms of process description are discussed and positive conclusions have been drawn for future applications of an advanced meteo-hydro model synergy. Keywords: RAPHAEL, modelling, forecasting, model coupling, PROMET-D, TOPMODEL

scholarly journals Overview of the European project FUMAPEX

2006 ◽  
Vol 6 (7) ◽  
pp. 2005-2015 ◽  
Author(s):  
A. Baklanov
Keyword(s):  
Air Pollution ◽  
Prediction Models ◽  
Meteorological Data ◽  
Weather Prediction ◽  
Urban Air Pollution ◽  
Population Exposure ◽  
Urban Air ◽  
Exposure Models ◽  
Numerical Weather Prediction Models

Abstract. The quality of the urban air pollution forecast critically depends on the mapping of emissions, the urban air pollution models, and the meteorological data. The quality of the meteorological data should be largely enhanced by using downscaled data from advanced numerical weather prediction models. These different topics, as well as the application of population exposure models, have traditionally been treated in distinct scientific communities whose expertise needs to be combined to enhance the possibilities of forecasting air pollution episodes in European cities. For this purpose the EU project "Integrated Systems for Forecasting Urban Meteorology, Air Pollution and Population Exposure'' (FUMAPEX) (http://fumapex.dmi.dk), involving 22 organizations from 10 European countries, was initiated. The main objectives of the project are the improvement of meteorological forecasts for urban areas, the connection of numerical weather prediction models to urban air pollution and population exposure models, the building of improved Urban Air Quality Information and Forecasting Systems, and their application in cities in various European climates. This paper overviews the project items and first two-years results, it is an introduction to the whole ACP issue.

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