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 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 ! 11 th IEEE International Conference on eScience – Munich, Germany Aug 31 – Sept 04, 2015
Motivation � 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. 11 th IEEE International Conference on eScience – Munich, Germany Aug 31 – Sept 04, 2015
NWP: meteorological phenomena 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 0 C, 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 0 C between 2011-2040, � 2 0 C between 2041-2070. 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. 11 th 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. • 11 th 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 3 rd of August. For example on July 31, 2011 the maximum air temperature in Meghri was 43.7 0 C due to the fact that the territory of Armenia was under the influence of the thermal depression. � On August 27 th , 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. 11 th 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 11 th 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. 11 th 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 11 th 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. 11 th IEEE International Conference on eScience – Munich, Germany Aug 31 – Sept 04, 2015
WRF Workflow 11 th 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. 11 th 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). 11 th 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. 11 th 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. 11 th 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. 11 th 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 of 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 . 11 th IEEE International Conference on eScience – Munich, Germany Aug 31 – Sept 04, 2015
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