Porting the COSMO Weather Model to Manycore CPUs

2019 ◽  
Author(s):  
Felix Thaler ◽  
Stefan Moosbrugger ◽  
Carlos Osuna ◽  
Mauro Bianco ◽  
Hannes Vogt ◽  
...  
Keyword(s):  
Weather Model

scholarly journals Accuracy of a Model-Free Algorithm for Temporal InSAR Tropospheric Correction

2021 ◽  
Vol 13 (3) ◽  
pp. 409
Author(s):  
Howard Zebker
Keyword(s):  
Time Series ◽  
Signal To Noise Ratio ◽  
Artifact Reduction ◽  
Interferometric Synthetic Aperture Radar ◽  
Spatial Properties ◽  
Model Free ◽  
Weather Model ◽  
Phase Variations ◽  
Source Of Error ◽  
Signal Correlation

Atmospheric propagational phase variations are the dominant source of error for InSAR (interferometric synthetic aperture radar) time series analysis, generally exceeding uncertainties from poor signal to noise ratio or signal correlation. The spatial properties of these errors have been well studied, but, to date, their temporal dependence and correction have received much less attention. Here, we present an evaluation of the magnitude of tropospheric artifacts in derived time series after compensation using an algorithm that requires only the InSAR data. The level of artifact reduction equals or exceeds that from many weather model-based methods, while avoiding the need to globally access fine-scale atmosphere parameters at all times. Our method consists of identifying all points in an InSAR stack with consistently high correlation and computing, and then removing, a fit of the phase at each of these points with respect to elevation. A comparison with GPS truth yields a reduction of three, from a rms misfit of 5–6 to ~2 cm over time. This algorithm can be readily incorporated into InSAR processing flows without the need for outside information.

scholarly journals A THREE DIMENSIONAL MODEL OF THE GULF OF ALASKA

1986 ◽  
Vol 1 (20) ◽  
pp. 193 ◽  
Author(s):  
Shiao-Kung Liu ◽  
Jan J. Leendertse
Keyword(s):  
Three Dimensional ◽  
Gulf Of Alaska ◽  
Vertical Shear ◽  
Dimensional Model ◽  
Three Dimensional Model ◽  
Weather Model ◽  
Closure Scheme ◽  
Vertical Grid ◽  
Northern Pacific ◽  
Horizontal Grid

This paper presents the development of a three dimensional model of the Gulf of Alaska. The model extends between the Vancouver Island and the Aleutian Islands covering approximatedly 1.5 million square kilometers over the northern Pacific Ocean. Formulated on an ellipsoidal horizontal grid and variable vertical grid, the model is schematized over a 81 x 53 x 10 grid structure. The solution scheme is implicit over the vertical and is programmed using one-dimensional dynamic array for the efficient use of machine storage. The turbulence closure scheme for the non-homogeneous vertical shear is formulated so that the potential and kinetic energetics are monitored and transferred in a closed form. The hydrodynamic model is coupled to a two-dimensional stochastic weather model and an oil-spill trajectory/weathering model. The former also simulates stochastically the cyclogenetic/cyclolytic processes within the modeled area. The paper also compares the computed results with the available field data. Good agreements are found in tidal amplitude and phases as well as currents.

scholarly journals Mitigating atmospheric noise for InSAR using a high resolution weather model

2006 ◽  
Vol 33 (16) ◽  
Author(s):  
J. Foster ◽  
B. Brooks ◽  
T. Cherubini ◽  
C. Shacat ◽  
S. Businger ◽  
...  
Keyword(s):  
High Resolution ◽  
Atmospheric Noise ◽  
Weather Model

Simulating mountain runoff with meso-scale weather model rainfall estimates: a New Zealand experience

2000 ◽  
Vol 239 (1-4) ◽  
pp. 19-32 ◽  
Author(s):  
R.P Ibbitt ◽  
R.D Henderson ◽  
J Copeland ◽  
D.S Wratt
Keyword(s):  
New Zealand ◽  
Weather Model ◽  
Meso Scale ◽  
Rainfall Estimates

A Streamflow and Water Level Forecasting Model for the Ganges, Brahmaputra, and Meghna Rivers with Requisite Simplicity

2018 ◽  
Vol 19 (1) ◽  
pp. 201-225 ◽  
Author(s):  
Wahid Palash ◽  
Yudan Jiang ◽  
Ali S. Akanda ◽  
David L. Small ◽  
Amin Nozari ◽  
...  
Keyword(s):  
Water Level ◽  
Lead Time ◽  
Large Scale ◽  
Predictive Ability ◽  
Large River ◽  
Modeling Framework ◽  
Forecasting Accuracy ◽  
Weather Model ◽  
And Performance ◽  
The Ganges

A forecasting lead time of 5–10 days is desired to increase the flood response and preparedness for large river basins. Large uncertainty in observed and forecasted rainfall appears to be a key bottleneck in providing reliable flood forecasting. Significant efforts continue to be devoted to developing mechanistic hydrological models and statistical and satellite-driven methods to increase the forecasting lead time without exploring the functional utility of these complicated methods. This paper examines the utility of a data-based modeling framework with requisite simplicity that identifies key variables and processes and develops ways to track their evolution and performance. Findings suggest that models with requisite simplicity—relying on flow persistence, aggregated upstream rainfall, and travel time—can provide reliable flood forecasts comparable to relatively more complicated methods for up to 10 days lead time for the Ganges, Brahmaputra, and upper Meghna (GBM) gauging locations inside Bangladesh. Forecasting accuracy improves further by including weather-model-generated forecasted rainfall into the forecasting scheme. The use of water level in the model provides equally good forecasting accuracy for these rivers. The findings of the study also suggest that large-scale rainfall patterns captured by the satellites or weather models and their “predictive ability” of future rainfall are useful in a data-driven model to obtain skillful flood forecasts up to 10 days for the GBM basins. Ease of operationalization and reliable forecasting accuracy of the proposed framework is of particular importance for large rivers, where access to upstream gauge-measured rainfall and flow data are limited, and detailed modeling approaches are operationally prohibitive and functionally ineffective.

TOMOREF operator as a tool to improve weather forecasts

2021 ◽  
Author(s):  
Natalia Hanna ◽  
Estera Trzcina ◽  
Maciej Kryza ◽  
Witold Rohm
Keyword(s):  
Data Assimilation ◽  
Weather Forecasting ◽  
Positive Impact ◽  
Precipitation Event ◽  
Initial State ◽  
Total Delay ◽  
Numerical Weather ◽  
Numerical Weather Model ◽  
Weather Model ◽  
Tomography Data

<p>The numerical weather model starts from the initial state of the Earth's atmosphere in a given place and time. The initial state is created by blending the previous forecast runs (first-guess), together with observations from different platforms. The better the initial state, the better the forecast; hence, it is worthy to combine new observation types. The GNSS tomography technique, developed in recent years, provides a 3-D field of humidity in the troposphere. This technique shows positive results in the monitoring of severe weather events. However, to assimilate the tomographic outputs to the numerical weather model, the proper observation operator needs to be built.</p><p>This study demonstrates the TOMOREF operator dedicated to the assimilation of the GNSS tomography‐derived 3‐D fields of wet refractivity in a Weather Research and Forecasting (WRF) Data Assimilation (DA) system. The new tool has been tested based on wet refractivity fields derived during a very intense precipitation event. The results were validated using radiosonde observations, synoptic data, ERA5 reanalysis, and radar data. In the presented experiment, a positive impact of the GNSS tomography data assimilation on the forecast of relative humidity (RH) was noticed (an improvement of root‐mean‐square error up to 0.5%). Moreover, within 1 hour after assimilation, the GNSS data reduced the bias of precipitation up to 0.1 mm. Additionally, the assimilation of GNSS tomography data had more influence on the WRF model than the Zenith Total Delay (ZTD) observations, which confirms the potential of the GNSS tomography data for weather forecasting.</p>

scholarly journals Naturally triggered persistent deep slab avalanches in western Canada Part I: avalanche characteristics and weather trends from weather stations

2016 ◽  
Vol 62 (232) ◽  
pp. 243-255 ◽  
Author(s):  
MICHAEL CONLAN ◽  
BRUCE JAMIESON
Keyword(s):  
Air Temperature ◽  
Classification Tree ◽  
Decision Aids ◽  
Companion Paper ◽  
Wind Loading ◽  
False Alarms ◽  
Western Canada ◽  
Weather Model ◽  
Weather Stations ◽  
Median Maximum

ABSTRACTA database of difficult-to-forecast natural persistent deep slab avalanches was analyzed to determine thresholds for parameters that contribute to their release in western Canada. The database included avalanche observations and weather station data. The avalanches were grouped based on their primary cause-of-release, either precipitation loading, wind loading, solar warming or air temperature warming using a multivariate classification tree, which first split using a solar warming parameter. The precipitation group had a median 24 h snowfall of 15 cm and 3 d snowfall of 38 cm at weather stations, mostly at or below treeline. These amounts were likely closer between 20–30 and 50–80 cm at alpine start zones. The wind loading group experienced the most wind-transported snow potential. The solar warming group had predicted solar warming of 5.2°C, 10 cm into the snowpack, on the days of release. The air temperature warming group experienced the highest median maximum air temperature (5°C) on the days of release. These thresholds may be useful to forecast the likelihood of similar avalanches with experienced-based forecasting or with decision aids, although many false alarms are possible. A companion paper, Part II, relates weather model data to avalanche occurrences.

Sign in / Sign up

Export Citation Format

Share Document