WRF-ARW Model for Prediction of High Temperatures in South and South - - PowerPoint PPT Presentation

WRF-ARW Model for Prediction of High Temperatures in South and South East Regions of Armenia

• H. Astsatryan, A. Shakhnazaryan,V. Sahakyan, Yu. Shoukourian,
• Z. Petrosyan, R. Abrahamyan, H. Melkonyan, V. Kotroni

Institute for Informatics and Automation Problem, Armenian State Hydrometeorological and Monitoring Service, National Observatory of Athens

Motivation

11th IEEE International Conference on eScience – Munich, Germany Aug 31 – Sept 04, 2015

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Current Situation Hhydrometeorological products, e.g. synoptic maps, forecasts, from the European Centre for Medium-range Weather Forecasting, UNISYS, COLA/IGES, GFS are used to produce weather forecasts for Armenia. Challenge Armenia is located on the very edge, that doesn’t allow the detailed analysis of the weather systems affecting the area. The outputs of global models are with coarse resolution, which represent only broad features and patterns and are able to reproduce processes in the large scale. Armenia, operates 48 meteorological stations, and only 3 meteorological station synoptic data are involved in global exchange

11th IEEE International Conference on eScience – Munich, Germany Aug 31 – Sept 04, 2015

Due to its complex relief and diversity of natural conditions Armenia is exposed to various types of hydrometeorological hazardous events, amongst it is worth noting heavy rainfall, strong winds, heat waves, hailstorms, snowfall, frosts, etc.

Motivation

11th IEEE International Conference on eScience – Munich, Germany Aug 31 – Sept 04, 2015

In recent years there have been increasing tendencies of dangerous meteorological phenomena, for instance due to the climate change the deviation of the mean temperature in Armenia during 1995-2013 increased by about 1.03 0C, relative to the stable mean temperature during 1961-1990. The analyses of show that the mean temperature in Armenia is predicted to increase by about 1.5 0C between 2011-2040, 2 0C between 2041-2070.

NWP: meteorological phenomena

It is crucial to deal with the desertification phenomenon in Armenia, as being located in the central part of the subtropical dry climate zone the territory of Armenia has all the characteristics of an arid region.

11th IEEE International Conference on eScience – Munich, Germany Aug 31 – Sept 04, 2015

Objective

• To study of high temperature, wind and precipitation regimes in

South and South East Regions of Armenia using the advanced implementation of the WRF model.

• To develop an early warning system for severe weather phenomena.

11th IEEE International Conference on eScience – Munich, Germany Aug 31 – Sept 04, 2015

Time Period

The number and the characteristics of annual hot days have been analyzed in 2011 and 2014, as: determine the frequency of recurrent tendency of hot days Summer 2011 - high temperature period from the 30th of July to the 3rd of August. For example on July 31, 2011 the maximum air temperature in Meghri was 43.7 0C due to the fact that the territory of Armenia was under the influence of the thermal depression. On August 27th, a cold front passed through the territory of Armenia with wavy perturbations that caused downpours of torrential rains accompanied by thunderstorms and hail in separate regions.

11th IEEE International Conference on eScience – Munich, Germany Aug 31 – Sept 04, 2015

Study Area

D1 - the major part of Europe and all the Caucasus and some parts of the Central Asia with 202x202 grid points at a 18-km resolution D2 - (6-km horizontal grid increment) covers the whole territory of Armenia with 97x70 grid points D3 - 145x91 grids at a 2-km resolution covering the southern regions, namely the territory of Ararat valley, Vayots Dzor, Syunik regions, Yerevan and partly Gegharkunik

11th IEEE International Conference on eScience – Munich, Germany Aug 31 – Sept 04, 2015

Study Area

The study area covers non only Ararat Plain, which is the one of the more important of the distinctive regions of Armenia surrounding with foothills and mountains, but also Vayotz Dzor, essentially the basin of the Arpa River and Syunik in the extreme southeast.

11th IEEE International Conference on eScience – Munich, Germany Aug 31 – Sept 04, 2015

Computing Infrastructure

Amrcluster – 128 cores (120 minutes each simulation), total – 500K CPU hours

11th IEEE International Conference on eScience – Munich, Germany Aug 31 – Sept 04, 2015

Computing Services

An interoperable web portal for satellite image processing Hrachya Astsatryan,et al., An Interoperable Cloud-based Scientific Gateway for NDVI Time Series Analysis, Elsevier Computer Standards & Interfaces, 2015, 31(40), pp. 79–84, doi: 10.1016/j.csi.2015.02.001. Hrachya Astsatryan, et al.,, An interoperable web portal for parallel geoprocessing of satellite image vegetation indices, Springer Earth Science Informatics, vol. 8, no. 2, June 2015, pp. 453-460, DOI: 10.1007/ s12145-014-0165-3. National Spatial Data Infrastructure

• Sh. Asmaryan, A. Saghatelyan, H. Astsatryan, et al., Paving the way

toward an environmental National Spatial Data Infrastructure in Armenia, South-Eastern European Journal of Earth Observation and Geomatics, Vo3, No3S, pp. 53-62, e-ISSN: 2241-1224, 2014.

11th IEEE International Conference on eScience – Munich, Germany Aug 31 – Sept 04, 2015

WRF Workflow

11th IEEE International Conference on eScience – Munich, Germany Aug 31 – Sept 04, 2015

Methodology

The weather forecasts are performed on a daily basis, using 1-way nesting strategy. The model uses vertical 31 eta_levels and geographic data resolution is 30 seconds. The model was initialized with the initial and boundary conditions of GFS at 00:00 UTC (local time on 04:00) For comparison the data were taken from all weather stations that passed through GTS every 3 hours (transmission time for 00,03,06,09,12,15,18.21 UTC). Since its maximum temperature usually reaches 12h UTC, then were taken to verify the actual data for 12h UTC. The Single-Moment 6-class Microphysics scheme and the convective parameterization scheme of Kain and Fritsch for the parent domain were selected.

• For the verification procedure, meteorological data received from

four weather stations located in the nested domain was used.

11th IEEE International Conference on eScience – Munich, Germany Aug 31 – Sept 04, 2015

Verification

The WRF outputs at the six neighbouring grid points that are closest to meteorological stations have been used for the following verifications of the model: Air temperature: t+12h forecast outputs of the model of D3. Wind: t+12h is used to verify the wind Precipitation: total daily amount (24-h accumulated precipitation). Verification scores: Correlation coefficients; standard deviations of the differences; BIAS measures the ratio of the frequency of forecast events to the frequency of observed events and it indicates whether the forecast system has a tendency to under predict (BIAS<1) or over predict.nts

• For the precipitation verification and analyses - bias score (BIAS),

probability of detection (POD), false alarm ratio (FAR) and threat score (CSI).

11th IEEE International Conference on eScience – Munich, Germany Aug 31 – Sept 04, 2015

Verification

• The entire study area was divided into sub-regions taking into

account morphological and population characteristics.

• A yellow, orange or red warning thresholds are set for this particular

region, depending on the severity of the event.

11th IEEE International Conference on eScience – Munich, Germany Aug 31 – Sept 04, 2015

Results

According to the verification results and the frequency of the warnings, the conclusion is that the daily maximum temperature is slightly overestimated by the model.

11th IEEE International Conference on eScience – Munich, Germany Aug 31 – Sept 04, 2015

Results

The verification and analyses of CSI indicated shows that the model provides enough accurate results of the amount of precipitation. The verification and analyses of Bias shows that the model forecast is close to the observed values in the stations Artashat (1.08), Meghri (1.22) and Yerevan (1.23), but for Armavir we receive an overestimated result, which is 1.47.

11th IEEE International Conference on eScience – Munich, Germany Aug 31 – Sept 04, 2015

Conclusion

The prototype of the early warning system is developed for the territory

• f Armenia, by dividing the study area in 10 sub-regions and defining

specific thresholds for issuing alerts for adverse weather phenomena The verification of the model is carried out by comparing the model results with observations from three automatic meteorological stations. For air temperature and wind speed, correlation coefficients and biases are calculated For precipitation amount, yes/no contingency tables are constructed for 4 specific thresholds and some categorical statistics are applied, showing that the prediction of precipitation in the area under study is generally satisfactory.

11th IEEE International Conference on eScience – Munich, Germany Aug 31 – Sept 04, 2015

Further activities & Acknoledgement

To carry out some predictive modelling under different climate change scenarious To implement ensemble forecasting cli To develop and Early Warning Systems in Armenia and beyond in order to help authorities to respond to both short-term/rapid onset climatic hazards. ACKNOWLEDGMENT

• VI-SEEM (VRE for regional Interdisciplinary communities in Southeast

Europe and the Eastern Mediterranean)

• ArmGrid ("Deployment of a National e-Infrastructure for Disaster Risk

Management")

11th IEEE International Conference on eScience – Munich, Germany Aug 31 – Sept 04, 2015