Application of Unified Model (UM) weather prediction data for rice disease forecast

Transcription

1 Application of Unified Model (UM) weather prediction data for rice disease forecast Ki Seok Do, Hyo-suk Kim, and Eun Woo Park National Center for Agricultural Meteorology Seoul National University Moon Il Ahn, Yong Soon Shin, Jong Sun Park, Joo Hyun Park, Wee Soo Kang, Sung Gi Kim, and Yong Kyu Han EPINET Co., LTD 1

2 Objectives Apply the UM data for plant disease forecast prior to pathogen infection Evaluation of UM data with reference to AWS data Evaluation of UM-based disease forecast with reference to AWS-based disease forecast Improve the 48 hour disease forecast Weather data: Digital Forecast (DF) Unified Model data (UM) Spatial resolution: 5km 1.5km Develop Internet URL and mobile application for disease forecast Develop an IT platform for ag-met information services Data Acquisition and Process System (DAPS)

3 - Use of weather data

4 Research outline Weather data acquisition Extraction of weather data Model application Model evaluation User interface AWS UM Temp Dew point temp Rainfall Rain hour Relative humidity Wind speed Light intensity Sunshine hour Blast (Modified BLAST) Sheath blight (SHBLGHT) Bacterial grain rot (BGRcast) AWS data Blast Sheath blight Bacterial grain rot UM data Blast Sheath blight Baterical grain rot Gridded data Map images Graphs & Tables Web-GIS Interface Model output post-process Data format conversion 4

5 Data for model evaluation Historical disease data 3 diseases of rice: blast, sheath blight and bacterial grain rot 30 sites Disease severity at 10 day intervals during May 20 th to Sep. 20 th in 2014 and 2015: Blast: % leaf area diseased Sheath blight: % vertical progression and % hills diseased Bacterial grain rot: % panicles diseased Weather data: estimated by IDW using AWS data from KMA Field plot observations 2 sites: Icheon and Naju Disease severity at 7~10 day intervals during Jun. 5 th to Oct. 30 th, 2016 AWS for weather monitoring UM data from KMA 36 hour forecast at 1.5km and 1 hour resolution Temperature, RH and rainfall 30 sites of disease data collection Field plot abd AWS in Icheon

6 AWS AWS AWS AWS AWS AWS Temperature( C) RH (%) Rainfall (mm) National Center of AgroMeteorology UM data vs. AWS data Daily average IDW of temperature, RH IDW and rainfall IDW data points from Icheon and Naju during Jun. 3 to Oct. 13, 2016 IDW IDW IDW Temperature ( ) RH (%) Rainfall (mm) R 2 = R 2 = R 2 = n= UM UM UM UM03 UM03 UM03 UM03 UM03 UM03 R 2 = R 2 = R 2 = UM15 UM15 UM15 UM15 UM15 UM15

7 5 types of input weather data sets Day t Day t hr +36 hr A B C (AWS: ~ 07KTC) D (AWS: ~ 19KTC) (UM t 03KTC: +5 ~ +36hr) (UM t 15KTC: +5 ~ +32hr) (UM t+1 03KTC: +5 ~ +20hr) (UM t+1 15KTC: +5 ~ +8hr) E (AWS: ~ 23KTC)

8 UM-based disease forecast vs. AWS-based disease forecast Disease forecast for tomorrow (day t+1) using UM15:00 of day t Two-way contingency table analysis (CTA) Data from 30 sites in 2014 and 2015: Blast: 10,500 data points for May 5 th to Oct. 31 st Sheath blight: 10,800 data points for May 5 th to Oct. 31 st Bacterial grain rot: 6,540 data points for Jul. 15 th to Oct. 31 st Disease forecast category: 4 risk levels Zero / Low / Intermediate / High Indices for CTA Probability of detection (POD) = H/(M+H) False alarm ratio (FAR) = F/(F+H) Critical success index (CSI) = H/(M+F+H) Accuracy (ACC) = (C+H)/(C+M+F+H) UM-based forecast Miss (M) False alarm (F) Hit (H) Correct negative (C) AWS-based forecast

9 - Blast Table. Two-way contingency table analysis to evaluate accuracy of rice blast forecasts based on the weather forecast by UM as compared with disease forecasts based on the observed weather data from 30 sites during May 5 th to Oct. 31 st in 2014 and Index Categorical score (%) Zero Low Inter-mediate High Hit Miss False alarm Correct negative POD FAR CSI ACC

10 - Sheath blight Table. Two-way contingency table analysis to evaluate accuracy of sheath blight forecasts based on the weather forecast by UM as compared with disease forecasts based on the observed weather data from 30 sites during May 5 th to Oct. 31 st in 2014 and Index Categorical score (%) Zero Low Inter-mediate High Hit Miss False alarm Correct negative POD FAR CSI ACC

11 - Bacterial grain rot Table. Two-way contingency table analysis to evaluate accuracy of bacterial grain rot forecasts based on the weather forecast by UM as compared with disease forecasts based on the observed weather data from 30 sites during May 5 th to Oct. 31 st in 2014 and Index Categorical score (%) Zero Low Inter-mediate High Hit Miss False alarm Correct negative POD FAR CSI ACC

12 Disease development in the field plots in 2016 Almost no development of rice blast ang bacterial grain rot in both Icheon and Naju due to record high temperature during summer in 2016 Sheath blight development NF: Conventional nitrogen fertilization HF: 2 times high nitrogen fertilization

13 National Center for AgroMeteorology Data Acquisition and Process System NCAM Observed Data FLUX Data collection Data Collection and Management System Monitoring system monitoring Administrator Web server Metadata Model info. Collection KMA Observed Data ASOS AWOS Forecast Short term Mid-term FTP server File server Data storage Extracted metadata DB or File Weather data Raw weather data Preconditioned data for model run Data distribution info. FLUX data Postprocessed data from model outputs Processing Data transfer Inquiry Making Transfer Data transfer Other systems for research NCAM-LAMP Estimation of water use in agricultural land Demonstration Specific report Typhon, etc. Image Radar data Numerical forecast UM Postprocessing Model running Preconditioning Store Model outputs Extract metadata Data transfer Running model program (using Python, Java, R script, etc.) Data reception Transform data Store input data Rice disease Model BLAST SHBLIGHT BGRcast Ginseng Model Microclimate Growth stage Alternaria blight Data transfer Rice disease info. Ginseng info. Water use info. Processing System for Agrometeorological Model Satellite Data for background map MODIS Landsat Supported Data Form NSIP, MLIT GOCI Excel ASCII XML JPEG JSON WMS Binary

14 Conclusion The UM data were different from the corresponding AWS data. However, they were acceptable enough to use for plant disease forecast. The contingency table analysis indicated that the UM-based disease forecast was equivalent to the AWS-based disease forecast. The disease data from field plots in 2016 suggested that the disease forecast models used in this study needs to be further evaluated with respect to their performance. The UM-based disease forecast would be useful to improve practical use of plant disease forecast information by farmers. Enhanced spatial resolution of the 48 hour forecast: 1.5km x 1.5km The IT platform, Data Acquisition and Process System (DAPS), is under construction for ag-met information services in Korea.

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