Determination of location-specific weather information for agronomic decision support

Patent metadata

Field	Value
Publication number	US-11719858-B2
Application number	US-201917055432-A
Country	US
Kind code	B2
Filing date	May 14, 2019
Priority date	May 16, 2018
Publication date	Aug 8, 2023
Grant date	Aug 8, 2023

Abstract

A method performed by at least one apparatus is inter alia disclosed, said method comprising: obtaining weather model data indicative of location-specific weather information for a first set of locations (26) on a first grid (28); obtaining an area of interest (30) associated to at least one user (32); obtaining and/or determining a second set of locations (34) based on a second grid (36) within said area of interest (30); obtaining measurement data on location-specific weather information of a measurement device associated to said at least one user located at a measurement location (38) within and/or proximate to said area of interest (30); and determining, based on at least said obtained weather model data and said obtained measurement data, location-specific weather information for said second set of locations (34) based on said second grid (36).

First claim

What is claimed is:

1. A method performed by at least one apparatus, said method comprising:
obtaining weather model data indicative of location-specific weather information for a first set of locations ( 26 ) on a first grid ( 28 );
obtaining an area of interest ( 30 ) associated to at least one user ( 32 );
obtaining and/or determining a second set of locations ( 34 ) based on a second grid ( 36 ) within said area of interest ( 30 );
obtaining measurement data on location-specific weather information of a measurement device associated to said at least one user located at a measurement location ( 38 ) within and/or proximate to said area of interest ( 30 );
determining, based at least partially on at least one machine learning process and on at least said obtained weather model data and said obtained measurement data, location-specific weather information for said second set of locations ( 34 ) based on said second grid ( 36 );
obtaining and/or determining a third set of locations ( 40 ) based on a third grid ( 42 ) within said area of interest ( 30 );
obtaining, based on at least one of a crop growth model and/or a crop growth measurement, surface topography data for at least one location within and/or proximate to said area of interest ( 30 ); and
determining, based at least partially on the at least one machine learning process and on said obtained surface topography data and said determined location-specific weather information for said second set of locations ( 34 ), location-specific weather information for said third set of locations ( 40 ) based on said third grid ( 42 ).

2. The method according to claim 1 , wherein said second grid ( 36 ) provides a spatial resolution being higher, in particular at least one order of magnitude higher than a spatial resolution of said first grid ( 28 ).

3. The method according to claim 1 , further comprising:
obtaining elevation model data indicative of a surface elevation for at least one location within and/or proximate to said area of interest ( 30 );
wherein said determining of said location-specific weather information for said second set of locations ( 34 ) is further based on said elevation model data.

4. The method according to claim 1 , wherein said determining of said location-specific weather information for said second set of locations ( 34 ) comprises a spatial interpolation of said weather model data on said second set of locations ( 34 ); and/or a spatial interpolation of said measurement data on said second set of locations ( 34 ).

5. The method according to claim 1 , further comprising:
determining location-specific bias information based on deviations of said obtained weather model data and/or said obtained measurement data;
wherein said determining of said location-specific weather information for said second set of locations ( 34 ) is further based on said location-specific bias information.

6. The method according to claim 5 , further comprising:
determining at least one field parameter of a statistical model based at least partially on said location-specific bias information, wherein said determining of said location-specific weather information for said second set of locations ( 34 ) is further based on said statistical model using said at least one field parameter.

7. The method according to claim 5 , further comprising:
carrying out at least one machine learning process based at least partially on said location-specific bias information to obtain at least one field parameter of a model for said location-specific bias information,
wherein said determining of said location-specific weather information for said second set of locations ( 34 ) is further based on said model for said location-specific bias information using said at least one field parameter.

8. The method according to claim 1 , wherein said determining of said location-specific weather information for said second set of locations comprises a spatial process model of said weather model data and said obtained measurement data on said second set of locations ( 34 ).

9. The method according to claim 1 , wherein said third grid ( 42 ) provides a spatial resolution being higher, in particular at least one order of magnitude higher than a spatial resolution of said second grid ( 36 ).

10. The method according to claim 1 , wherein said surface topography data is further based on at least:
radar data and/or lidar data.

11. The method according to claim 1 , wherein said surface topography data is indicative of surface elevation and/or surface roughness.

12. The method according to claim 1 , wherein said location-specific weather information for said first, second and/or third set of locations ( 26 , 34 , 40 ) is indicative of at least one of precipitation, wind speed, wind direction, temperature, dew point temperature, air pressure, geopotential, relative humidity, sunshine duration, global radiation and/or solar irradiation;
and
wherein said location-specific weather information for said first, second and/or third set of locations ( 26 , 34 , 40 ) is indicative of a current weather status and/or a weather forecast.

13. The method according to claim 1 , wherein said location-specific weather information for said first, second and/or third set of locations ( 26 , 34 , 40 ) is indicative of at least one of near surface wind profile,
near surface humidity profile,
water transfer of crops, and/or
near surface water balance.

14. The method according to claim 1 , wherein said first grid ( 28 ) comprises a spatial resolution for locations in the order of 10 km to 100 km.

15. The method according to claim 1 , wherein said second grid ( 36 ) comprises a spatial resolution for locations in the order of 500 m to 2 km.

16. The method according to claim 1 , wherein said third grid ( 42 ) comprises a spatial resolution for locations in the order of 5 m to 50 m.

17. The method according to claim 1 , wherein said location-specific weather information for said second and/or third set of locations ( 34 , 40 ) is used in a crop growth model, a crop pathogenic risk model and/or a pest model.

Assignees

• Basf Agro Trademarks Gmbh

Inventors

• Wahabzada Mirwaes
• Hoffmann Holger
• Hill Eva
• Peters Ole
• Kerkhoff Christian
• Ilbasi Umit Baran
• Shankar Priyamvada

Classifications

• G01W1/10 Primary

  Devices for predicting weather conditions (computers per se G06; display devices G09) · CPC title

• G01W1/06

  giving a combined indication of weather conditions (catathermometers for measuring "cooling value" related either to weather conditions or to comfort of other human environment G01W1/17; computers per se G06) · CPC title

• G06F30/27

  using machine learning, e.g. artificial intelligence, neural networks, support vector machines [SVM] or training a model · CPC title

• G01W2201/00

  Weather detection, monitoring or forecasting for establishing the amount of global warming · CPC title

• G01W2203/00

  Real-time site-specific personalized weather information, e.g. nowcasting · CPC title