Introduction to SARWeather Ólafur Rögnvaldsson – IMR/Belgingur [email protected] Logi Ragnarsson...

Introduction to SARWeather

Ólafur Rögnvaldsson – IMR/[email protected]

Logi Ragnarsson – IMR/[email protected]

Cloud Computing Education series

Acknowledgement• SARWeather is a joint research project led by IMR/Belgingur, in

collaboration with NOAA/ESRL, the University of Bergen, and the private companies GreenQloud and DataMarket. To ensure maximum usability for SAR operators, SARWeather is developed in close collaboration with ICE-SAR and the Civil Protection Department of the Icelandic Police.

• SARWeather was initially funded in part by grant number 550-025 (Vejrtjeneste for Søberedskab) from NORA and by the European Commission under the 7th Community Framework Programme for Research and Technological Development (GalileoCast). GalileoCast is managed by GSA, the European GNSS Supervisory Authority.

• Current development of SARWeather is funded in part by the Icelandic Technical Development Fund – RANNÍS

• Development related to the SUMO has in part been funded by the COST project ES0802

SARWeather – www.sarweather.com


http://www.sarweather.com/

• Atmospheric Models• Global vs. regional• The WRF model• Importance of resolution

• Current Crisis Response System• SARWeather demonstration

• Use of observations from UAS’s• Conclusions and future work

Overview




Atmospheric models

Set of equations that describe the atmospheric flow and need to be integrated forward in time to produce a weather forecast



Global

Regional


Atmospheric models

Set of equations that describe the atmospheric flow and need to be integrated forward in time to produce a weather forecast


SARWeather – www.sarweather.comRegional

Initial and boundary data come from global models


• Open source model developed in collaboration of• NCAR, NOAA, FSL, AFWA, NRL, Univ. of Oklahoma & FAA

• Five development teams with sixteen workgroups• More than 160 official developer• Additional development by academic and governmental

institutions in the US and abroad• Very large user community, excellent support

• Non-hydrostatic• Regional and global applications• Wide range of scales for both real time and idealized applications• Optional data assimilation; 3D-VAR, 4D-VAR, EnKFM, and FDDA• Has been modified for volcanic applications• Includes a dust module (re-suspension of dust/ash)

The WRF atmospheric modelCloud Computing Education series



Importance of high resolutionCloud Computing Education series

Importance of high resolution

How does the weather model “see” this mountain?




Importance of high resolutionVatnajökull icecap, max height is 1675m. Top height of Mt. Öræfajökull is only 880m

True height of Mt. Öræfajökull is 2119m



At 1km resolution the max height is 2020m. Top height of Mt. Öræfajökull is now 1945m



9 km resolution

10 15 20 25 30 35 40 45 m/s





3 km resolution

10 15 20 25 30 35 40 45 m/s





1 km resolution


Simulation results are also affected by the choice of parameterization schemes

Rögnvaldsson, Ó., Bao, J.-W., Ágústsson, H., and Ólafsson, H.: Downslope windstorm in Iceland – WRF/MM5 model comparison, Atmos. Chem. Phys., 11, 103-120, doi:10.5194/acp-11-103-2011, 2011.





How is the wind flow over this mountainous peninsula simulated at 9 and 1 km resolution?





When model resolution is increased to 1 km the true complexity and strength of the wind field becomes apparent

Very high resolution – 1 km

Medium resolution – 9 km




Why not always use 1km resolution?

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100

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Grid size [km]

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Need 1000-times more CPU power to simulate a 1 km resolution forecast than a 10 km one for the same region!




• You only need high very high resolution once in a while?• Computer clouds (e.g. Azure, EC2 and

GreenQloud) are starting to offer HPC service• Offers great scalability• Relatively cheap• And there is already a solution out there

What if




• Quality weather information help improve decision making

• Current CRS uses the WRF model and consists of a • Backend and Frontend

• Frontend is called SARWeather• Easy to use• Fast• Flexible model output and presentation• CF and ArcGIS compliant output files• Interactive and static maps

Crisis Response System




Simulated and observed surface windson 15 July 2009 at 15 UTC

WRF at a resolution of 500 m forced with ECMWF-data on model levels.

Observed surface winds in red

Mt. Esja

High resolution not always sufficient

Model simulates a see-breeze that is not seen in observations




SUMO and WRF

The SUMO (Small Unmanned Meteorological Observer) can measure winds, humidity, pressure, and temperature in a vertical profile up to a 4km height


Can be operated in cold climates



SUMO and WRF

This data can be assimilated with the WRF weather forecast




The SUMO-data is incorporated into the WRF-simulation, via obs-nudging

SUMO and WRF Cloud Computing Education series



Simulated and observed surface windson 15 July 2009 at 13 UTC

WRF at a resolution of 500 m forced with ECMWF-data on model levels and SUMO data

Observed surface winds in red

Effects of additional observations

The flow structure is now in much better agreement with available observations




Simulated flow in N-S section across Mt. Esja

A major difference in flow pattern extending far above mountain top leve

23 km

Wind speed, ranging from 0 to 12 m/s

Effects not just at the surface Cloud Computing Education series



Effects can be far reaching

CTRL - ObsNudge

Marius O. Jonassen, Haraldur Ólafsson, Hálfdán Ágústsson, Ólafur Rögnvaldsson, and Joachim Reuder (2012). Improving a high resolution numerical weather simulation by assimilating data from an unmanned aerial system. Accepted for publication in Monthly Weather Review

“Substantial improvements of winds, temperatures and humidity in the region are achieved”




Transmitting data from the field


Three profiles used to nudge the forecast at times 11:55, 12:58 and 14:38 UTCSite altitudes ~ 1300 m.a.s.l.Profile heights ~ 2000 m.a.g.l.From 860hPa to 650/680hPa

One profile used for comparison at 16:30 UTC

Observations made over Myrdalsjokull ice cap in South Iceland on 17 May 2012Data can be transmitted via 3G mobile connection



Transmitting data from the field


Observations made over Myrdalsjokull ice cap in South Iceland on 17 May 2012

Obs in blackWRF - noSUMO in redWRF - withSUMO in blue

Profile used for nudging @ 14:38 UTC

Profile used for comparison @ 16:30 UTC

850 hPa

750 hPa

850 hPa

750 hPa

TemperatureWind-speed

Temperature

Wind-speed

-20°C -10°C 0°C 5m/s 10m/s

-20°C -10°C 0°C 5m/s 10m/s

Problems with low-level windspeed and a cold bias 2hrs after last profile



The SUMO has been equipped with an optical dust sensor

Additional sensors

GP2Y1010AU0F is a dust sensor by optical sensing system:• An infrared emitting diode (IRED) and an

phototransistor are diagonally arranged into the device

• It detects the reflected light of dust in air• Especially effective to detect very fine particle• In addition it can distinguish smoke from house

dust by pulse pattern of output voltage

Saturation at about 500μg/m3




The SUMO dust sensor has been tested in France and Iceland

Preliminary results




The SUMO dust sensor has been tested in France and Iceland

Preliminary results

Sensor is now being calibrated and tested with ash from Mt. Eyjafjallajökull

AscendingDescending




Calibration – preliminary results• The sensor (BU) readings show high

sensitivity in the range 125 – 300. After that the sensitivity is rather low in the range 350 to 700.

• Using the meter for ash surveillance for jet aircrafts, the best thing would be to designate below 300 as “safe” but above 400 as “unsafe”




Various uses of model output

To correctly simulate distribution of pollutants, it is very important to correctly simulate the atmospheric conditions close to the source.

Data courtesy of Prof. Saji Hameed at the University of Aizu, Fukushima.




Various uses of model output

Output from SARWeather could also be used as input to coastal waves and circulation and storm surge models such as the coupled SWAN+ADCIRC system

Simulated maximum water level for hurricane Gustav, September 2008. Taken from www.adcirc.org



http://www.adcirc.org/


Misc. datasources (optional)

Current research and development Cloud Computing Education series



Current research and development Cloud Computing Education series



• Model resolution is important• Especially in the vicinity of complex terrain

• On-Demand CR system has been developed• Called SARWeather – www.sarweather.com • Data from SARWeather can be used as a driver for a number of

other modeling systems• Additional observations can improve the simulation

• Vertical profiles made by the SUMO, radiosondes or other means

• The SUMO is a low-cost system with many advantages• Proof of concept before investing in a more durable and

expensive UAS• Additional sensors are being added to the system• Is currently being integrated to the SARWeather CR system

Conclusions





• Continue improving usability• Re-occurring forecasts• Add output at different vertical levels• Add weather parameters for aviation• Visibility, icing potential, turbulence severity

• Better support for mobile devices• Location based services

• Add support for different modeling systems, e.g.• Dispersion models• Wave and storm surge models• Flash-flood modeling

• What would you like to see?• Interest in developing add-on solutions for SARWeather?

Future work, collaboration?




Introduction to SARWeather Ólafur Rögnvaldsson – IMR/Belgingur [email protected] Logi Ragnarsson...

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Transcript of Introduction to SARWeather Ólafur Rögnvaldsson – IMR/Belgingur [email protected] Logi Ragnarsson...