Ultra-High Resolution Time Traveling AgMet Information from Seeding to Harvesting - seamless data for prospect estimation of crop yields - Dec. 5, 2016 Jai-ho Oh & Kyung-Min Choi Dept. Env. & Atmos. Sci., Pukyong National University, Busan, Korea jhoh@pknu.ac.kr
Introduction Seamless AgMet data from past to future Nano-scale AgMet data from past to future
Structure diagram Mar. Apr. May. Jun. Jul. Aug. Sep. Oct. Observation-based Synthetic data (1km 1km) 2014 2015 2016 Jan. Feb. Mar. Apr. May. Jun. Jul. Aug. Prediction data New Prediction 2017 Prediction data
Method Prediction Global or Mesoscale Model (GME-40 km Grid) Prediction data 3hr interval u, v, P, w T, I, r h Topography (1km) Downscaling Quantitative Temperature Model (1 km Grid) 1 km high-resolution topography data Quantitative Precipitation Model (1 km Grid) Synthetic high resolution (1 km ) data based on observation data High Resolution Temperature & Rainfall Data
Method Prediction Global Prediction data Model : Global Model GME V2.30 Horizontal & Vertical Resolution : 40 km/40 layers Method for Seasonal Prediction Time-lag Method - Prediction run with daily SST & sea ice forcing (10 Ensemble) I. C. : ECMWF Operational Analysis data B. C. : NOAA OI Monthly Global SST data ECMWF Operational Analysis sea ice data Synthesis of Observation Synthetic high resolution (1x1 km ) data based on observation QPM(Quantitative Precipitation Model) QTM(Quantitative Temperature Model) Observation Data South Korea : AWS/ASOS & MERRA North Korea : MERRA
International Workshop AgMet and GIS Applications, 5 th -9 th Dec., 2016, Jeju, Korea Synthesis of Observation QTM (Quantitative Temperature Model) QTM, QPM consider the effect of small-scale topography DEM (Digital Elevation Model) data Grid size : 1km 1km Topography (1km) recalculating observed 2m temp. above sea-level pressure T slp j = T obs j + Γ H dem (j) interpolating to 1km T slp j T intp j recalculating temperature on altitude of DEM T qtm j = T intp j Γ H dem (j)
Synthesis of Observation QPM (Quantitative Precipitation Model) Govern Eq. Q r t = u Q r x v Q r y w Q r z + 1 ρ z ρv rq r + P 1 E 1 Q r : raindrop mixing ratio P 1 : condensation E 1 : evaporation V r : fall speed of raindrop u, v, w : wind components ρ : density of air Separating Eq. to Mesoscale field and small-scale perturbation field Rainfall intensity (Q r + Q r ) = u (Q r + Q r ) (Q r + Q r v ) w (Q r + Q r ) t x y z + 1 ρ z ρv r(q r + Q r ) + (P 1 + P 1 ) (E 1 + E 1 ) I = V r (Q r + Q r )
Synthesis of Observation Data North Korea Data Time Interval Horizontal Resolution Vertical Resolution MERRA (NASA) vertical : 3hrs horizontal : 1hr 1.25 1.25 0.667 0.5 72 Levels South Korea Data Time Interval Station AWS & ASOS (KMA) 1hr, daily 494 / 93 Data Time Interval Horizontal Resolution Vertical Resolution MERRA (NASA) vertical : 3hrs horizontal : 1hr 1.25 1.25 0.667 0.5 72 Levels
Time traveling climate information Ex) 2m Temperature 2016 Jan. Feb. Mar. Apr. May. Jun. Jul. Mar. Apr. May. Jun. Jul. Aug. Sep. Oct. Prediction data New Prediction New Prediction New Prediction New Prediction Observation-based Synthetic Observation-based data (1km 1km) Synthetic data (1km 1km) Aug. Observation-based Synthetic data (1km 1km) the same as above
Output variables Variables Data for Crop Model Variable name Level Long name (units) albdo surface (solar) shortwave albedo at the surface (%) ssr surface surface solar radiation balance (W/m**2) pres surface surface pressure on model orography (Pa) tmp 850hPa temperature at 850hPa (K) tmax 2m maximum temperature at (K) tmin 2m minimum temperature at (K) pr surface precipitation (kg/m**2) uwind 10m zonal wind at 10m above ground (m/s) vwind 10m meridional wind at 10m above ground (m/s) shum surface specific humidity (kg/kg) *Data set is depending on the user.
Application for Africa Daily Data in African 3 Regions for Crop Model 2m Maximum Temperature [K] 2m Minimum Temperature [K] Precipitation [kg/m**2] 850 hpa Temperature [K] Solar Radiation Balance at the Surface [W/m**2] (Solar) Shortwave Albedo at the Surface [%] Specific Humidity [kg/kg] Surface Pressure [Pa] 10m Wind Speed [m/s] 10m Meridional Wind [m/s] 10m Zonal Wind [m/s]
Nano-scale Agro-meteorological Information Ethiopia Awash River Basin Agro-ecological Zones 1 km x 1 km scale Nano-Climate Earth Surface = 2πR 2 = 2 x 3.14 x 6350 x 6350 = 2.53 x 10 8 km 2 1 km 2 4 x 10-9 Earth s Surface
Nano-scale Agro-meteorological Information Ethiopia Awash River Basin Agro-ecological Zones
Nano-scale Agro-meteorological Information The High Reaches of Myanmar The Reaches of Myanmar ( Lahe & Nan Yun) One of Major City ( Kalaw & Inle lake)
Conclusions Ultra-high resolution prediction system provides useful data to agricultural community in detail. This system has the following advantage: 1 Providing daily essential variables for crop model. 2 Providing ultra-high resolution synthetic data for ungagged sites 3 Providing time traveling updated nano-scale agrometeorological data in combination of the past, present and future data
International Workshop on AgMet and GIS Applications, Data 5 th -9 th points Dec., 2016, = 672,661 Jeju, Korea 1km Resolution Future change of 2m temperature 0.05 0.55 50.0 Suyoung-gu Data points = 19 Busan Data points = 2,001 7.5 0.05 0.4
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