CN112541655B - Atmospheric analysis method for regional wind energy resource refined assessment requirements - Google Patents
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Abstract
The invention discloses an atmospheric analysis method aiming at regional wind energy resource refinement assessment requirements, which utilizes the advantage of an ERA5 of a global advanced atmospheric analysis system on satellite data assimilation, combines the effect of regional high-resolution topography, adopts an assimilation method combining Nudging and 3D-VAR, introduces local meteorological office and energy resource institution anemometer tower data, and prepares the atmospheric analysis data with 2 kilometer resolution in the region.
Description
Technical Field
The invention relates to the technical field of atmospheric analysis, in particular to an atmospheric analysis method aiming at regional wind energy resource refined assessment requirements.
Background
The atmospheric data re-analysis refers to a process of forming four-dimensional meteorological elements containing space-time variation of all elements by utilizing all acquired historical observation and detection data to perform data assimilation analysis through a meteorological numerical mode data assimilation technology. The atmospheric analysis data provides important information sources for fully displaying the space-time distribution and the change of atmospheric motion, understanding the motion law of atmospheric circulation more deeply, understanding the cause and mechanism of climate change deeply and evaluating water circulation and energy balance more reliably, provides the most important and even irreplaceable data tools for modern climate change research, provides important support for the research of atmospheric science and related fields, and greatly promotes the development of modern atmospheric science. At present, analytical data have been widely used in many research fields such as climate monitoring and season forecast, climate change rate and change, global and regional water circulation and energy balance, and atmospheric mode assessment.
Wind energy resource assessment is the statistics, analysis and application of high spatial-temporal resolution to wind speeds in an area. Therefore, wind energy resource assessment is severely dependent on wind speed and wind direction data which can respond to high spatial-temporal resolution, and atmospheric analysis data is regarded as the most important data source for wind energy resource assessment. The current wind energy resource assessment method is to scale down the mesoscale numerical mode of the global re-analysis data line, and then scale down the meteorological element field with mesoscale including the wind field to microscale (say 200 m resolution) by adopting a statistical and dynamic microscale model.
Disclosure of Invention
The invention aims to solve the technical problems that the resolution ratio is lower in the existing analysis method and the influence of terrain on a wind field cannot be reflected.
In order to solve the technical problems, the invention adopts the following technical scheme: an atmospheric analysis method for the regional wind energy resource refined assessment requirement comprises the following steps:
1) Acquiring the latest generation global analysis data ERA5 of the European medium-term weather forecast center;
2) Acquiring high-resolution geographical terrain basic data required by a scale WRF mode in an area to be evaluated;
3) Acquiring global GTS observation, and automatic station observation and anemometer tower observation data in a country or region where an area to be evaluated is located;
4) Quality control is carried out on the obtained observation data, and the observation data are processed into a preBUFR format file every hour;
5) Designing a double unidirectional nested WRF mode grid; the horizontal resolution of the outer area is 12km, and the country or region where the area to be evaluated is located is completely covered; the horizontal resolution of the inner area is 2km, covering the area to be evaluated;
6) Starting Geogrid in the WRF mode WPS module to generate a geographical terrain lattice point field required by a mode area;
7) Ungrib, metgrid in the WRF mode WPS module is started, and a mode lattice element field is generated by ERA5 data hour by hour;
8) Generating a mode initial field and a boundary field by using a real module in the WRF mode and using the mode lattice point data generated in the 7), and simultaneously generating a lattice point equalization field wrffdda_d01 required in lattice point four-dimensional assimilation (grid-FDDA);
9) Starting the simulation of the WRF outer region for 5 hours, and adopting a lattice point FDDA assimilation method in the simulation to enable a simulation field to approach an ERA5 analysis field;
10 When the WRF outer area is simulated to the 5 th hour, stopping the assimilation of the grid point FDDA, starting the simulation of the inner area, and adopting a unidirectional nesting mode for the outer area and the inner area;
11 When the inner area and the outer area of the WRF are simulated to the 6 th hour, starting to output a simulation field hour by hour until the 17 th hour is finished;
12 Using NCEP GSI assimilation system, using the observation data generated in 4) to assimilate three-dimensional variation data of the simulation field generated in 11), and generating an atmospheric analysis field for 12 hours from 5 th to 17 th hours;
repeating the steps 7) -12), and obtaining the regional atmosphere analysis field of the last 5 years.
Furthermore, the grid point FDDA method enables the initial field of the area mode and the boundary condition to approach infinitely, and a four-dimensional variation method is adopted to assimilate the atmospheric analysis field with coarser resolution of conventional meteorological observation data, radar, satellite and other remote sensing data; stopping FDDA 1 hour before starting the re-analysis simulation, so that the mode can simulate the atmospheric physical process according to the terrain effect with high resolution and the atmospheric interaction with high resolution, and after generating a high-resolution simulation field, assimilating meteorological and anemometer tower observation data by a three-dimensional variation method to enable the simulation field to be closer to actual observation.
From the technical scheme, the invention has the following advantages: the mature mesoscale mode, the mature and rapid Nudging and 3D-VAR assimilation technology, the global re-analysis data of the assimilated satellite data, the meteorological observation data and the observation data of the energy industry are applied to comprehensively analyze and form a high-resolution wind field and other meteorological element fields which can reflect the influence of complex terrains.
Drawings
FIG. 1 is a flow chart of the present invention.
Detailed Description
In the embodiment, taking China as an example, the advantage of an ERA5 of a global advanced atmospheric analysis system for satellite data assimilation is utilized, the effect of regional high-resolution topography is combined, and the method of assimilation by combining Nudging and 3D-VAR is adopted to introduce the data of a wind tower of a China weather office and an energy agency to manufacture the atmospheric analysis data with the resolution of 2 kilometers in the whole country.
As shown in fig. 1, this embodiment includes the steps of:
1) Acquiring the latest generation global analysis data ERA5 of the European medium-term weather forecast center;
2) Obtaining high-resolution geographical topography basic data required by a mesoscale WRF mode;
3) Acquiring global GTS observation, china automatic station observation and wind tower observation data;
4) Quality control is carried out on the obtained observation data, and the observation data are processed into a preBUFR format file every hour;
5) A dual unidirectional nested WRF-mode grid is designed. The horizontal resolution of the outer area is 12km, and the Chinese area is completely covered; the horizontal resolution of the inner area is 2km, covering the area to be evaluated;
6) Starting Geogrid in the WRF mode WPS module to generate a geographical terrain lattice point field required by a mode area;
7) Ungrib, metgrid in the WRF mode WPS module is started, and a mode lattice element field is generated by ERA5 data hour by hour;
8) Generating a mode initial field and a boundary field by using a real module in the WRF mode and using the mode lattice point data generated in the 7), and simultaneously generating a lattice point equalization field wrffdda_d01 required in lattice point four-dimensional assimilation (grid-FDDA);
9) Starting the simulation of the WRF outer region for 5 hours, and adopting a lattice point FDDA assimilation method in the simulation to enable a simulation field to approach an ERA5 analysis field;
10 When the WRF outer area is simulated to the 5 th hour, stopping the assimilation of the grid point FDDA, starting the simulation of the inner area, and adopting a unidirectional nesting mode for the outer area and the inner area; the FDDA method is used in combination with a three-dimensional variation assimilation method. The grid point FDDA method enables the initial field of the area mode and the boundary condition to approach infinitely, and a four-dimensional variation method is adopted to assimilate the rough resolution atmospheric analysis field of conventional meteorological observation data, radar, satellite and other remote sensing data; stopping FDDA 1 hour before starting the re-analysis simulation, so that the mode can simulate the atmospheric physical process according to the terrain effect with high resolution and the atmospheric interaction with high resolution, and after generating a high-resolution simulation field, assimilating meteorological and anemometer tower observation data by a three-dimensional variation method to enable the simulation field to be closer to actual observation.
11 When the inner area and the outer area of the WRF are simulated to the 6 th hour, starting to output a simulation field hour by hour until the 17 th hour is finished;
12 Using NCEP GSI assimilation system, using the observation data generated in 4) to assimilate three-dimensional variation data of the simulation field generated in 11), and generating an atmospheric analysis field for 12 hours from 5 th to 17 th hours;
13 Repeating steps 7) -12), and acquiring the atmosphere of the area in the analysis field for the last 5 years.
On the basis of the scheme, the global hour-by-hour analysis data ERA5 in the step 1 is a global hour-by-hour analysis data set generated by upgrading the European middle weather forecast center (ECMWF) on the basis of ERA-interim re-analysis data; the global GTS observation data in the step 3 are data distributed by world weather organization (WMO) through a global communication system; the WRF mode system is a us weather research and forecast mode system (The Weather Research and Forecasting Model, WRF) GSI assimilation system is an assimilation system in us weather service and research.
Claims (1)
1. An atmospheric analysis method for the regional wind energy resource refined assessment requirement comprises the following steps:
1) Acquiring the latest generation global analysis data ERA5 of the European medium-term weather forecast center;
2) Acquiring high-resolution geographical terrain basic data required by a scale WRF mode in an area to be evaluated;
3) Acquiring global GTS observation, and automatic station observation and anemometer tower observation data in a country or region where an area to be evaluated is located;
4) Quality control is carried out on the obtained observation data, and the observation data are processed into a preBUFR format file every hour;
5) Designing a double unidirectional nested WRF mode grid; the horizontal resolution of the outer area is 12km, and the country or region where the area to be evaluated is located is completely covered; the horizontal resolution of the inner area is 2km, covering the area to be evaluated;
6) Starting Geogrid in the WRF mode WPS module to generate a geographical terrain lattice point field required by a mode area;
7) Starting ungrib, metgrid in WRF mode WPS module generates mode lattice point for hour-by-hour ERA5 data
A pixel field;
8) Generating a mode initial field and a boundary field by using a real module in the WRF mode and using the mode lattice point data generated in the 7), and simultaneously generating a lattice point equalization field wrffdda_d01 required in lattice point four-dimensional assimilation (grid-FDDA);
9) Starting the simulation of the WRF outer region for 5 hours, and adopting a lattice point FDDA assimilation method in the simulation to enable a simulation field to approach an ERA5 analysis field;
10 When the WRF outer area is simulated to the 5 th hour, stopping the assimilation of the grid point FDDA, starting the simulation of the inner area, and adopting a unidirectional nesting mode for the outer area and the inner area; the grid point FDDA method enables the initial field of the area mode and the boundary condition to approach to the atmospheric analysis field with coarser resolution by adopting a four-dimensional variation method to assimilate conventional meteorological observation data, radar and remote sensing data of satellites; stopping FDDA 1 hour before starting the re-analysis simulation, so that the mode carries out the simulation of the atmospheric physical process according to the terrain effect with high resolution and the atmospheric interaction with high resolution, and after generating a simulation field with high resolution, assimilating meteorological and anemometer tower observation data by a three-dimensional variation method to enable the simulation field to be closer to actual observation;
11 When the inner area and the outer area of the WRF are simulated to the 6 th hour, starting to output a simulation field hour by hour until the 17 th hour is finished;
12 Using NCEP GSI assimilation system, using the observation data generated in 4) to assimilate three-dimensional variation data of the simulation field generated in 11), and generating an atmospheric analysis field for 12 hours from 5 th to 17 th hours;
13 Repeating steps 7) -12) to obtain the regional atmosphere analysis field of the last 5 years.
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| CN110275224A (en) * | 2019-05-24 | 2019-09-24 | 兰州大学 | Refine Meteorological element close to the ground forecast system and its forecasting procedure |
| CN110968929A (en) * | 2018-09-30 | 2020-04-07 | 北京金风慧能技术有限公司 | Wind power plant wind speed prediction method and device and electronic equipment |
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| CN110968929A (en) * | 2018-09-30 | 2020-04-07 | 北京金风慧能技术有限公司 | Wind power plant wind speed prediction method and device and electronic equipment |
| CN110275224A (en) * | 2019-05-24 | 2019-09-24 | 兰州大学 | Refine Meteorological element close to the ground forecast system and its forecasting procedure |
| CN110196457A (en) * | 2019-06-18 | 2019-09-03 | 国网河南省电力公司电力科学研究院 | A kind of ground sudden strain of a muscle Data Assimilation method and system for Severe Convective Weather Forecasting |
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