USDA s Operational GIS & related processes for International Weather & Crop Assessements

Transcription

1 USDA s Operational GIS & related processes for International Weather & Crop Assessements International Workshop on Agromet and GIS Applications for Agricultural Decision Making Jeju, South Korea December 6, 2016 Eric Luebehusen USDA/OCE/WAOB Meteorologist

2 At the USDA-WAOB (), our focus is naturally on global weather and crop production.

3 My international AOR stretch from Europe and nrn Africa into Russia, Uzbekistan, and the Middle East.

4 A group of 5 meteorologists at USDA/WAOB covers the globe, including the U.S.

5 Our primary responsibility is to provide support for decisions related to the monthly release of the WASDE.

6 As a supplement to our support of the WASDE, we produce the Weekly Weather & Crop Bulletin in close coordination with NOAA/CPC. This covers the U.S. & international crop areas.

7 We also provide a detailed Weekly Weather Briefing every Thursday for Economists and other USDA staff to apprise them of the current developments. GIS is a big part of all these efforts.

8 USDA/WAOB has 2 USDM authors. USDA/WAOB staff implemented the 2003 USDM conversion to GIS, and since 2008 have spearheaded the ongoing effort to get weather and hydrological GIS data directly into the USDM editing process. This was discussed in greater detail yesterday.

9 Primary Data Source: WMO through NOAA/CPC

10 RegPlot* JAWFProb* Software for Data Management & Analysis *Software developed, updated, and maintained in-house.

11 RegPlot* JAWFProb* A quick look at our Oracle database, the backbone of our GIS work for the WAOB *Software developed, updated, and maintained in-house.

12 * Random sampling of available daily weather data An extensive network of WMO station data is downloaded from the U.S. NWS, Climate Prediction Center and housed at USDA in an Oracle database, accessible through GIS. We can not redistribute this data.

13 * Random sampling of available daily weather data In addition, we are also downloading other sources of in-situ data for our in-house database and GIS applications.

14 In Australia, the WMO station density is particularly poor in major southeastern Wheat areas.

15 Our Australia analyst (H. Shannon) now downloads and saves data from COOP weather stations; WMO stations number in the hundreds, while COOP stations number in the thousands. These data are added to the Oracle database daily.

16 Monsoon Seasonal Rainfall (% of Normal): June 1 September 23, 2015 Legend District-level rainfall data for India is downloaded for a more detailed analysis of the progress of the summer monsoon (B. Morris).

17 Rain Gauge (900+) Supplemental data obtained by NWS-CPC from the Servicio Meteorológico Nacional (Mexico) are incorporated into the weekly rainfall chart created for the Weekly Weather and Crop Bulletin and are provided separately to USDA analysts for their analysis of crop weather impacts. (M. Brusberg) WMO (~70)

18 We likewise have discovered and use web-based services (such as OGIMET) to augment our GIS database & QC efforts.

19 Wget Like many of our operations, we schedule Wget to download this data to our database daily. Wget is essential to our operations.

20 10,000+ stations Dates back to GB of data and counting Additional SQL database server for domestic data

21 GIS with WMO Data at USDA OCE/WAOB

22 Python code runs automatically to interpolate the dbf data into a corresponding Geotiff. Files overwrite daily, weekly, and monthly, so as to not fill up the server. X:\Applications\Operational\AutoWxAnalysis\Current\WMO_Data.shp Python X:\Applications\Operational\AutoWxAnalysis\Current\Precip.tif Python

23 Taking it a step further, Python code is automatically run every day to create a series of pre-set maps for USDA meteorologists international areas of responsibility, covering daily, weekly, and monthly time periods. X:\Applications\Operational\AutoWxAnalysis\Current\Precip.tif Python Python

24 Automated GIS products allow 5 meteorologists at USDA to cover global weather and crop impacts for the. This effort includes weekly weather briefings for USDA staff; most of these maps are automatically produced using GIS and Python code.

25 In addition to the automated maps/products, macros/programs have been written to allow for interactive data extrapolation and interpretation within ArcMap for episodic events.

26 We augment our in-house WMO station dataset with gridded GIS data from the Global Precipitation Climatology Centre (GPCC), and produce concurrent maps with the WMO data.

27 Y:\GPCC\Monthly\CURRENT\1st-Guess\Current-Pcp.txt The process involves Wget to download ascii data, Python to convert to a geotiff, & Python to create the regional maps. Y:\GPCC\Monthly\CURRENT\1st-Guess\Current-Pcp.tif

28 To fully utilize GIS and the weather data, it is imperative to know where crops are grown; meteorologists at USDA/WAOB have downloaded and processed hundreds of crop production GIS dataset for international areas of responsibility.

29 Crop Calendars in GIS format (courtesy U. Wisc/SAGE) help USDA/WAOB further finetune our analyses by helping us estimate crop stage of development, a critical component to weather and yield/production impacts.

30 Global 4km VHI data (Geotiff) is downloaded automatically every Friday using Wget. Python code was written to compute the yearto-year change and create the maps which are then dropped onto our server.

31 Based on 13 years of data ( ) Vegetation Health Index vs. Wheat Yields South Australia Yield (vs. trend) R 2 = 0.92 VHI data Week 41 (7 day period ending Oct 14) Note Prelim data available Oct 15 Final data available Nov 25 VHI filling Our Australia analyst (H. Shannon) has demonstrated a high degree of success using the VHI data (GIS) for estimating Australia wheat yields.

32 Knowing crop stage is vital to crop and weather assessments. Growing Degree Day crop stage estimates are mapped in GIS (top left) or plotted using the planting dates supplied in GIS format (right).

33 To further illustrate a recent detailed episodic assessment using GIS: Last year s extreme heat in Europe is plotted over the corn production data (below) in conjunction with the mean planting dates (right)

34 Corn planting ~ late April The combination of the following GIS data.. - Corn Areas - Planting Dates - Maximum Temperature with additional data analysis within Excel lets meteorologists at USDA provide timely weather/crop impact assessments. (100.4 F) (100.4 F) Tassel/Silk

35 ftp://ftp.cpc.ncep.noaa.gov/fews/cmorph/tif/ We also download satellite-derived precipitation estimates (CMORPH) from the NWS Climate Prediction Center (geotiff). This data is run through Python code to automatically produce regional maps across the globe.

36 CMORPH Geotiff Python Blended Geotiff WMO Geotiff Our Asia analyst (B. Morris) developed Python code to create a blended precipitation product using CMORPH (satellite) and WMO (station) data.

37 More and more international GIS weather data/imagery is available through WMS, which we also use at USDA/WAOB for our daily operations.

38 In conclusion, a wide array of GIS data sources and approaches are employed at the USDA-WAOB. Automation of GIS data acquisition and processing is key. This allows a staff of 5 to cover global weather and crop impacts in a timely manner to support USDA s monthly market-sensitive report.

39 Eric Luebehusen USDA/OCE/WAOB Meteorologist USDM Author