Method and apparatus for producing ground vegetation input data for global climate change prediction model

Patent metadata

Field	Value
Publication number	US-11693151-B2
Application number	US-202117476735-A
Country	US
Kind code	B2
Filing date	Sep 16, 2021
Priority date	Dec 4, 2020
Publication date	Jul 4, 2023
Grant date	Jul 4, 2023

Abstract

This application relates to an input data generating apparatus for generating forcing data used as input data for a climate change prediction model. In one aspect, the apparatus includes a memory storing instructions and a processor configured to, by executing the instructions, collect new ground type data from land-use harmonization (LUH) data that is restored through history database of the global environment (HYDE) and provided by the coupled model inter-comparison project (CMIP). The processor may also collect existing ground type data calculated by an existing model in a previous phase of the CMIP, generate aggregated ground type data by data-aggregating the new ground type data and the existing ground type data, with priority to the new ground type data. The processor may further generate the forcing data from the aggregated ground type data by performing a distortion correction on a data distortion that occurs during the data aggregation.

First claim

What is claimed is:

1. An input data generating apparatus for generating forcing data used as input data for a climate change prediction model, the input data generating apparatus comprising:
a memory storing instructions; and
a processor configured to, by executing the instructions;
communicate data with a coupled model inter-comparison project (CMIP) model database storing a CMIP model;
retrieve existing ground type data from the CMIP model stored in the CMIP model database, wherein:
the existing ground type data has been generated at a first point in time,
the existing ground type data comprises vegetation types of data and non-vegetation types of data each arranged in grid,
the vegetation types of data including one or more of a broad leaf tree type, a needle leaf tree type, a C3 type, a C4 type, or a shrub type, and
the non-vegetation types of data including one or more of an urban type, an inland-water type, a bare-soil type, and an ice type, is arranged in each grid;

communicate data with a land-use harmonization (LUH) data database, wherein the LUH data database stores LUH data having a first grid size;
determine that the first gird size of the LUH data is not compatible with a predetermined climate change prediction model;
generate new ground type data from the LUH data stored in the LUH data database by converting the first grid size into a second grid size that is compatible with the predetermined climate change prediction model, such that the new ground type data has the second grid size, wherein:
the new ground type data comprises the C3 type and the C4 type selected from the vegetation types and the urban type selected from the non-vegetation types, and
the new ground type data reflects changes in ground vegetation according to human activities and time lapse at least between the first point in time and a second point in time later than the first point in time;

generate aggregated ground type data by data-aggregating the new ground type data and the existing ground type data such that:
the C3 type and the C4 type of the new ground type data are selected without selecting the C3 type and the C4 type from the vegetation types of data of the existing ground type data,
the urban type of the new ground type data is selected without selecting the urban type from the non-vegetation types of data of the existing ground type data,
the C3 type and the C4 type of the new ground type data are combined with one or more of the broad leaf tree type, the needle leaf tree type, or the shrub type selected from the vegetation types of data of the existing ground type data to generate aggregated vegetation types of data, and
the urban type of the new ground type data is combined with one or more of the inland-water type, the bare-soil type, or the ice type selected from the non-vegetation types of data of the existing ground type data to generate aggregated non-vegetation types of data,

determine whether the new ground type data and the existing ground type data conflict each other in one or more of the aggregated vegetation types of data or the aggregated non-vegetation types of data;
in response to determining that the new ground type data and the existing ground type data conflict each other, adjust one or more types of the existing ground type data, without adjusting the new ground type data, to generate the forcing data; and
apply the generated forcing data as an input to the climate prediction model.

2. The input data generating apparatus of claim 1 , wherein, when the new ground type data is collected, the processor is configured to collect the new ground type data having a resolution of 1.875°×1.25° by performing a grid conversion on the LUH data having a resolution of 0.25°×1.25°.

3. The input data generating apparatus of claim 1 , wherein, when the new ground type data is collected, the processor is configured to collect the C3 type and C4 type data based on grassland data including pasture and natural from among detailed items of the LUH data.

4. The input data generating apparatus of claim 1 , wherein the forcing data includes an area ratio of each of the aggregated vegetation and non-vegetation types of data calculated based on grid areas of the aggregated vegetation and non-vegetation types of data.

5. The input data generating apparatus of claim 1 , wherein, in adjusting the one or more types of the existing ground type data to generate the forcing data, the processor is configured to:
determine whether a sum of a grid area of the C3 type and a grid area of the C4 type exceeds a total grid area of the aggregated vegetation types of data; and
in response to determining that the sum exceeds the total grid area of the aggregated vegetation types, subtract an exceeding area from a grid area of the bare-soil type of the aggregated non-vegetation types of the existing ground type data.

6. A method of generating input data for generating forcing data used as input data for a climate change prediction model, the method being performed by a processor executing instructions stored in a memory, the method comprising:
communicating data with a coupled model inter-comparison project (CMIP) model database storing a CMIP model;
retrieving existing ground type data from the CMIP model stored in the CMIP model database, wherein:
the existing ground type data has been generated at a first point in time,
the existing ground type data comprises vegetation types of data and non-vegetation types of data each arranged in grid,
the vegetation types of data including one or more of a broad leaf tree type, a needle leaf tree type, a C3 type, a C4 type, or a shrub type, and
the non-vegetation types of data including one or more of an urban type, an inland-water type, a bare-soil type, and an ice type, is arranged in each grid;

communicating data with a land-use harmonization (LUH) data database, wherein the LUH data database stores LUH data having a first grid size;
determining that the first gird size of the LUH data is not compatible with a predetermined climate change prediction model;
generating new ground type data from the LUH data stored in the LUH data database by converting the first grid size into a second grid size that is compatible with the predetermined climate change prediction model, such that the new ground type data has the second grid size, wherein:
the new ground type data comprises the C3 type and the C4 type selected from the vegetation types and the urban type selected from the non-vegetation types, and
the new ground type data reflects changes in ground vegetation according to human activities and time lapse at least between the first point in time and a second point in time later than the first point in time;

generating aggregated ground type data by data-aggregating the new ground type data and the existing ground type data such that:
the C3 type and the C4 type of the new ground type data are selected without selecting the C3 type and the C4 type from the vegetation types of data of the existing ground type data,
the urban type of the new ground type data is selected without selecting the urban type from the non-vegetation types of data of the existing ground type data,
the C3 type and the C4 type of the new ground type data are combined with one or more of the broad leaf tree type, the needle leaf tree type, or the shrub type selected from the vegetation types of data of the existing ground type data to generate aggregated vegetation types of data, and
the urban type of the new ground type data is combined with one or more of the inland-water type, the bare-soil type, or the ice type selected from the non-vegetation types of data of the existing ground type data to generate aggregated non-vegetation types of data;
determining whether t

Assignees

• Nat Institute Of Meteorological Sciences

Inventors

• Sung Hyun Min
• Boo Kyung On
• Byun Young Hwa

Classifications

• G06F30/20

  Design optimisation, verification or simulation (optimisation, verification or simulation of circuit designs G06F30/30) · CPC title

• G01W2201/00

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

• G06Q10/04

  Forecasting or optimisation specially adapted for administrative or management purposes, e.g. linear programming or "cutting stock problem" (market predictions or forecasting for commercial activities G06Q30/0202) · CPC title

• G06F16/583

  using metadata automatically derived from the content · CPC title

• G01W1/18

  Testing or calibrating meteorological apparatus · CPC title